From 48c22f28b4703b7ca1040f9d8d94c3d51db9b066 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Mon, 11 Feb 2019 14:30:40 +0100 Subject: [PATCH 01/65] added delta estimation heuristic, tested on iris --- jupyter/bc_utils.py | 29 ++++++++++++++++++++ jupyter/iris.ipynb | 67 ++++++++++++++++++++++++++------------------- 2 files changed, 68 insertions(+), 28 deletions(-) diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index 3b87893..a50bb36 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -1,5 +1,7 @@ import pandas as pd import numpy as np + +from scipy.spatial import distance from matplotlib import pyplot @@ -179,3 +181,30 @@ def delta_medoids_full(df, delta, similarity_measure): print("delta_medoids_full algorithm ended after " + str(t) + " iterations.") return pd.DataFrame(list(representatives[t]), columns=df.columns.values) + +def estimate_delta(df, similarity_measure): + """Estimate delta for given dataset and similarity measure. + + Uses heuristic by picking 3 points from given dataframe - first, last and + middle one. Then it calculates distance to each other point in the dataset + and stores them. It returns the median * 1.05 value as a estimate delta value + for given dataset""" + similarities = np.array([]) + + ref_points = [df.iloc[0].values, + df.iloc[len(df.index) / 2].values, + df.iloc[len(df.index) - 1].values] + + for ref_point in ref_points: + for row in df.iterrows(): + point = tuple(row[1]) + + #skipping current ref_point + if point == tuple(ref_point): + continue + + sim = distance.cosine(point, ref_point) + similarities = np.append(similarities, sim) + + delta = np.median(similarities) + return delta * 1.05 #TODO store this value somewhere better diff --git a/jupyter/iris.ipynb b/jupyter/iris.ipynb index 5dba02f..f85d4f8 100644 --- a/jupyter/iris.ipynb +++ b/jupyter/iris.ipynb @@ -8,7 +8,12 @@ "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt \n", - "import pandas as pd \n", + "import pandas as pd\n", + "\n", + "import bc_utils as butils\n", + "\n", + "from scipy.spatial import distance\n", + "\n", "from sklearn.model_selection import train_test_split \n", "from sklearn.preprocessing import StandardScaler \n", "from sklearn.neighbors import KNeighborsClassifier " @@ -17,35 +22,38 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "# Assign colum names to the dataset \n", - "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class'] \n", - " \n", "# Read dataset to pandas dataframe \n", - "dataset = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\", names=names) \n", - " \n", - "X = dataset.iloc[:, :-1].values \n", - "y = dataset.iloc[:, 4].values \n", - " \n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) \n", - " \n", - "scaler = StandardScaler() \n", - "scaler.fit(X_train) \n", - " \n", - "X_train = scaler.transform(X_train) \n", - "X_test = scaler.transform(X_test) \n", - " \n", - "classifier = KNeighborsClassifier(n_neighbors=5) \n", - "y_train\n", - "#classifier.fit(X_train, y_train) \n", - " \n", - "#y_pred = classifier.predict(X_test) \n", - " \n", - "#from sklearn.metrics import classification_report, confusion_matrix \n", - "#print(confusion_matrix(y_test, y_pred)) \n", - "#print(classification_report(y_test, y_pred)) " + "df = pd.read_csv(\"iris.csv\")\n", + "\n", + "df.head() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#creating test datasets for iris\n", + "names = ['setosa', 'versicolor', 'virginica']\n", + "\n", + "dataframes = {}\n", + "for name in names:\n", + " tmp_df = df[df['species'] == name].iloc[:, :-1]\n", + " dataframes[name] = butils.TestDf(tmp_df)\n", + "\n", + "#trying to estimate delta\n", + "delta_df = dataframes['setosa'].train_df\n", + "\n", + "result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.cosine), distance.cosine)\n", + "result" ] }, { @@ -53,7 +61,10 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "result2 = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.cosine), distance.cosine)\n", + "result2" + ] } ], "metadata": { From a1492da9d02208c38d3fcc826dd01f19c2dab06b Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Mon, 11 Feb 2019 15:52:54 +0100 Subject: [PATCH 02/65] added classifyPoints method for iris and cleaned up blobs2d --- jupyter/blobs2d.ipynb | 41 ------------- jupyter/delta-medoids.ipynb | 111 ++++-------------------------------- jupyter/iris.ipynb | 110 ++++++++++++++++++++++++++++++++--- 3 files changed, 112 insertions(+), 150 deletions(-) diff --git a/jupyter/blobs2d.ipynb b/jupyter/blobs2d.ipynb index 08e0944..bda4422 100644 --- a/jupyter/blobs2d.ipynb +++ b/jupyter/blobs2d.ipynb @@ -234,47 +234,6 @@ "#print(learning_datasets[0].train_df)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def delta_medoids_one_shot(in_df, delta):\n", - " representatives = np.empty(1) #representative points of cluster\n", - " clusters = np.array(1) #clusters\n", - " \n", - " #here starts RepAssign\n", - " for point in in_df:\n", - " dist = float(\"inf\")\n", - " representative = None\n", - " \n", - " for rep in representatives: #needs optimalization\n", - " if distance.euclidean(point, rep) <= dist: #here add distance measure as parameter\n", - " representative = rep\n", - " dist = distance.euclidean(point, rep)\n", - " \n", - " if dist <= delta:\n", - " #add point to clusterrepr\n", - " cluster_repr.append(point)\n", - " else:\n", - " representative = point\n", - " representatives = np.array(point)\n", - " cluster_repr = np.array(point)\n", - " clusters.append(cluster_repr)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "delta_medoids_one_shot(learning_datasets[0].train_df, 10)\n", - "test = learning_datasets[0].train_df.iloc[0]\n", - "print(type(test))" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/jupyter/delta-medoids.ipynb b/jupyter/delta-medoids.ipynb index e220ff9..d5a2040 100644 --- a/jupyter/delta-medoids.ipynb +++ b/jupyter/delta-medoids.ipynb @@ -35,8 +35,8 @@ "metadata": {}, "outputs": [], "source": [ - "X, y = make_blobs(n_samples=300, centers=2, cluster_std=1.9, n_features=2)\n", - "df = pd.DataFrame(dict(x=X[:,0], y=X[:,1]))" + "X, y = make_blobs(n_samples=300, centers=2, cluster_std=1.9, n_features=5)\n", + "df = pd.DataFrame(dict(a=X[:,0], b=X[:,1],c=X[:,2], d=X[:,3],e=X[:,4]))" ] }, { @@ -63,88 +63,7 @@ }, "outputs": [], "source": [ - "plt = df.plot(x='x',y='y',kind='scatter', title='Test Cluster')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def find_best_cluster_representative(cluster, similarity_measure):\n", - " #input: np.array(of points)\n", - " #returns a tuple\n", - " \n", - " min_sum = float(\"inf\")\n", - " best_repr_index = None\n", - " for i in range(len(cluster)):\n", - " distance_sum = 0;\n", - " for point in cluster:\n", - " distance_sum += similarity_measure(cluster[i], point)\n", - " if (distance_sum > min_sum):\n", - " break\n", - " if(distance_sum < min_sum):\n", - " min_sum = distance_sum\n", - " best_repr_index = i\n", - " \n", - " return cluster[best_repr_index]\n", - "\n", - "def delta_medoids_full(df, delta, similarity_measure): \n", - " \"\"\"Returns subset of input DataFrame, that is a good representation \n", - " of given data. \n", - " \n", - " This is a simplified delta-medoids algorithm. It finds out the \n", - " representatives in one pass through the input data. Final representatives \n", - " depend on the ordering of input data. \n", - " \n", - " :param df: in data \n", - " :type df: pandas.DataFrame \n", - " :param delta: maximum distance of points to be considered similar \n", - " :type delta: float \n", - " :param similarity_measure: similarity function to be used in algorithm \n", - " :type similarity_measure: scipy.spacial.distance \n", - " \n", - " :Example: \n", - " >>> TODO\"\"\"\n", - " t = 0\n", - " representatives = {}\n", - " representatives[t] = set()\n", - " clusters = {}\n", - " \n", - " while True:\n", - " print(\"\\n=========== running for t = \" + str(t) + \"============\")\n", - " t = t + 1\n", - " \n", - " #================== RepAssign starts ================== \n", - " for row in df.iterrows():\n", - " dist = float(\"inf\")\n", - " represenative = None\n", - " \n", - " point = tuple(row[1]) \n", - " \n", - " for rep in clusters.keys():\n", - " #finding the closest representative to current point\n", - " if similarity_measure(point, rep) <= dist:\n", - " representative = rep\n", - " dist = similarity_measure(point, rep) \n", - " if dist <= delta:\n", - " clusters[representative] = np.vstack((clusters[representative], point))\n", - " else:\n", - " clusters[point] = np.array(point, ndmin=2) \n", - " #================== RepAssign ends ===================\n", - " \n", - " representatives[t] = set()\n", - " for cluster in clusters.values():\n", - " representative = find_best_cluster_representative(cluster, similarity_measure)\n", - " print(representative)\n", - " representatives[t].add(tuple(representative))\n", - " #print(representatives[t])\n", - " \n", - " if representatives[t] == representatives[t-1]:\n", - " break\n", - " \n", - " return pd.DataFrame(list(representatives[t]), columns=df.columns.values)" + "#plt = df.plot(x='x',y='y',kind='scatter', title='Test Cluster')" ] }, { @@ -155,8 +74,8 @@ }, "outputs": [], "source": [ - "cluster_repr_old = butils.delta_medoids_one_shot(df, 1.5, distance.euclidean)\n", - "cluster_repr = delta_medoids_full(df, 1.5, distance.euclidean)" + "cluster_repr_old = butils.delta_medoids_one_shot(df, 0.2, distance.cosine)\n", + "cluster_repr = butils.delta_medoids_full(df, 0.02, distance.cosine)" ] }, { @@ -165,28 +84,20 @@ "metadata": {}, "outputs": [], "source": [ - "cluster_repr.plot(x='x',y='y',kind='scatter', title='cluster_repr')\n", - "cluster_repr_old.plot(x='x',y='y',kind='scatter', title='cluster_repr_old')" + "cluster_repr" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ - "a = set([1, 2, 4, 5])\n", - "a = list(a)\n", - "a = pd.DataFrame(a)\n", - "a" + "#cluster_repr.plot(x='x',y='y',kind='scatter', title='cluster_repr')\n", + "#cluster_repr_old.plot(x='x',y='y',kind='scatter', title='cluster_repr_old')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/jupyter/iris.ipynb b/jupyter/iris.ipynb index f85d4f8..c4deefb 100644 --- a/jupyter/iris.ipynb +++ b/jupyter/iris.ipynb @@ -29,8 +29,7 @@ "source": [ "# Read dataset to pandas dataframe \n", "df = pd.read_csv(\"iris.csv\")\n", - "\n", - "df.head() " + "df " ] }, { @@ -46,14 +45,108 @@ "\n", "dataframes = {}\n", "for name in names:\n", - " tmp_df = df[df['species'] == name].iloc[:, :-1]\n", + " tmp_df = df[df['species'] == name]\n", " dataframes[name] = butils.TestDf(tmp_df)\n", "\n", - "#trying to estimate delta\n", - "delta_df = dataframes['setosa'].train_df\n", + "full_test_df = pd.DataFrame()\n", + "for name in names:\n", + " full_test_df = full_test_df.append(dataframes[name].test_df)\n", + "\n", + "#changing last column name to 'cluster' #TODO do it better\n", + "cols = list(full_test_df.columns[:-1])\n", + "cols.append('cluster')\n", + "full_test_df.columns = cols" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#using delta_medoids_full algorithm\n", + "results = {}\n", + "for name in names:\n", + " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", + "\n", + " result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.cosine), distance.cosine)\n", + " result['cluster'] = name\n", + " results[name] = result\n", + " \n", + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#using delta_medoids_one_shot algorithm\n", + "results2 = {}\n", + "for name in names:\n", + " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", + "\n", + " result = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.cosine), distance.cosine)\n", + " result['cluster'] = name\n", + " results2[name] = result\n", + " \n", + "print(results2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#creating training DataFrames for comparing oneshot and full delta medoids algorithm\n", + "train_delta_medoids_full = pd.DataFrame()\n", + "train_delta_medoids_one_shot = pd.DataFrame()\n", + "\n", + "for name in names:\n", + " train_delta_medoids_full = train_delta_medoids_full.append(results[name])\n", + " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(results2[name])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def classifyPoints(ref_df, test_df):\n", "\n", - "result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.cosine), distance.cosine)\n", - "result" + " X_train = ref_df.iloc[:, :-1].values\n", + " y_train = ref_df.iloc[:, -1].values\n", + " X_test = test_df.iloc[:, :-1].values\n", + " y_test = test_df.iloc[:, -1].values\n", + "\n", + " from sklearn.preprocessing import StandardScaler \n", + " scaler = StandardScaler() \n", + " scaler.fit(X_train)\n", + "\n", + " X_train = scaler.transform(X_train) \n", + " X_test = scaler.transform(X_test)\n", + "\n", + " from sklearn.neighbors import KNeighborsClassifier \n", + " classifier = KNeighborsClassifier(n_neighbors=5)\n", + " classifier.fit(X_train, y_train)\n", + "\n", + " y_pred = classifier.predict(X_test)\n", + "\n", + " from sklearn.metrics import classification_report, confusion_matrix \n", + " print(confusion_matrix(y_test, y_pred)) \n", + " print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classifyPoints(train_delta_medoids_full, full_test_df)" ] }, { @@ -62,8 +155,7 @@ "metadata": {}, "outputs": [], "source": [ - "result2 = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.cosine), distance.cosine)\n", - "result2" + "classifyPoints(train_delta_medoids_one_shot, full_test_df)" ] } ], From 2fa9c2ec3ed674651d304f080c41f154938cf7ac Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Tue, 12 Feb 2019 13:30:11 +0100 Subject: [PATCH 03/65] bugfix: not using similarity_measure passed to finding delta function --- jupyter/bc_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index a50bb36..d2194c7 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -203,7 +203,7 @@ def estimate_delta(df, similarity_measure): if point == tuple(ref_point): continue - sim = distance.cosine(point, ref_point) + sim = similarity_measure(point, ref_point) similarities = np.append(similarities, sim) delta = np.median(similarities) From d9eaa75f845f7d386e817903b58fbc64b6bfc806 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Tue, 12 Feb 2019 13:33:38 +0100 Subject: [PATCH 04/65] added new datasets, improved generation notebook new dataset: blobs.csv new dataset: blobs_3d.csv new dataset: noisy_circles.csv new dataset: noisy_circles_3d.csv --- jupyter/blobs.csv | 1501 ++++++++++++++++++++++++++++++++ jupyter/blobs_3d.csv | 1501 ++++++++++++++++++++++++++++++++ jupyter/iris.csv | 2 +- jupyter/make_2d_datasets.ipynb | 117 --- jupyter/make_datasets.ipynb | 235 +++++ jupyter/noisy_circles.csv | 1501 ++++++++++++++++++++++++++++++++ jupyter/noisy_circles_3d.csv | 1501 ++++++++++++++++++++++++++++++++ 7 files changed, 6240 insertions(+), 118 deletions(-) create mode 100644 jupyter/blobs.csv create mode 100644 jupyter/blobs_3d.csv delete mode 100644 jupyter/make_2d_datasets.ipynb create mode 100644 jupyter/make_datasets.ipynb create mode 100644 jupyter/noisy_circles.csv create mode 100644 jupyter/noisy_circles_3d.csv diff --git a/jupyter/blobs.csv b/jupyter/blobs.csv new file mode 100644 index 0000000..a397ef8 --- /dev/null +++ b/jupyter/blobs.csv @@ -0,0 +1,1501 @@ +,x,y,cluster +0,5.867498067335642,8.177151880030342,0 +1,5.613699815229969,9.932955265811792,0 +2,7.225084277857703,10.448861938921507,0 +3,6.76282254631617,0.6051453524386279,1 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d92c695..0000000 --- a/jupyter/make_2d_datasets.ipynb +++ /dev/null @@ -1,117 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating Blobs Problem\n", - "To manipulate the center of the cluster use the property of ```cluster_std```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "from sklearn.datasets.samples_generator import make_blobs\n", - "from matplotlib import pyplot\n", - "from pandas import DataFrame\n", - "\n", - "X, y = make_blobs(n_samples=100, centers=2, cluster_std=1.0, n_features=2)\n", - "df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", - "colors = {0:'red', 1:'blue'}\n", - "fix, ax = pyplot.subplots()\n", - "grouped = df.groupby('label')\n", - "for key, group in grouped:\n", - " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", - " label=key, color=colors[key])\n", - "pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating Moons Datasets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import make_moons\n", - "X, y = make_moons(n_samples=100, noise=0.2)\n", - "df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", - "fix, ax = pyplot.subplots()\n", - "grouped = df.groupby('label')\n", - "for key, group in grouped:\n", - " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", - " label=key, color=colors[key])\n", - "pyplot.show()\n", - "\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating Circles" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import make_circles\n", - "from matplotlib import pyplot\n", - "from pandas import DataFrame\n", - "# generate 2d classification dataset\n", - "X, y = make_circles(n_samples=100, noise=0.15)\n", - "# scatter plot, dots colored by class value\n", - "df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", - "colors = {0:'red', 1:'blue'}\n", - "fig, ax = pyplot.subplots()\n", - "grouped = df.groupby('label')\n", - "for key, group in grouped:\n", - " group.plot(ax=ax, kind='scatter', x='x', y='y', label=key, color=colors[key])\n", - "pyplot.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/jupyter/make_datasets.ipynb b/jupyter/make_datasets.ipynb new file mode 100644 index 0000000..dffc43f --- /dev/null +++ b/jupyter/make_datasets.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating Blobs Problem\n", + "To manipulate the center of the cluster use the property of ```cluster_std```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets.samples_generator import make_blobs\n", + "from matplotlib import pyplot\n", + "from pandas import DataFrame\n", + "\n", + "X, y = make_blobs(n_samples=100, centers=2, cluster_std=1.0, n_features=2)\n", + "df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", + "colors = {0:'red', 1:'blue'}\n", + "fix, ax = pyplot.subplots()\n", + "grouped = df.groupby('label')\n", + "for key, group in grouped:\n", + " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", + " label=key, color=colors[key])\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Moons Datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_moons\n", + "X, y = make_moons(n_samples=100, noise=0.2)\n", + "df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", + "fix, ax = pyplot.subplots()\n", + "grouped = df.groupby('label')\n", + "for key, group in grouped:\n", + " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", + " label=key, color=colors[key])\n", + "pyplot.show()\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Circles" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_circles\n", + "from matplotlib import pyplot\n", + "from pandas import DataFrame\n", + "# generate 2d classification dataset\n", + "X, y = make_circles(n_samples=100, noise=0.15)\n", + "# scatter plot, dots colored by class value\n", + "df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", + "colors = {0:'red', 1:'blue'}\n", + "fig, ax = pyplot.subplots()\n", + "grouped = df.groupby('label')\n", + "for key, group in grouped:\n", + " group.plot(ax=ax, kind='scatter', x='x', y='y', label=key, color=colors[key])\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#import time\n", + "#import warnings\n", + "\n", + "import numpy as np\n", + "#import matplotlib.pyplot as plt\n", + "\n", + "from sklearn import datasets # cluster, mixture\n", + "#from sklearn.neighbors import kneighbors_graph\n", + "#from sklearn.preprocessing import StandardScaler\n", + "#from itertools import cycle, islice\n", + "\n", + "np.random.seed(0)\n", + "\n", + "# ============\n", + "# Generate datasets. We choose the size big enough to see the scalability\n", + "# of the algorithms, but not too big to avoid too long running times\n", + "# ============\n", + "n_samples = 1500\n", + "noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,\n", + " noise=.05)\n", + "noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)\n", + "blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)\n", + "no_structure = np.random.rand(n_samples, 2), None\n", + "\n", + "# Anisotropicly distributed data\n", + "random_state = 170\n", + "X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)\n", + "transformation = [[0.6, -0.6], [-0.4, 0.8]]\n", + "X_aniso = np.dot(X, transformation)\n", + "aniso = (X_aniso, y)\n", + "\n", + "# blobs with varied variances\n", + "varied = datasets.make_blobs(n_samples=n_samples,\n", + " cluster_std=[1.0, 2.5, 0.5],\n", + " random_state=random_state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "noisy_circles = blobs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.scatter([i[0] for i in noisy_circles[0]], [i[1] for i in noisy_circles[0]])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# USE TO CREATE 2D DATASETS\n", + "import pandas as pd\n", + "\n", + "d = {'x': [i[0] for i in noisy_circles[0]],\n", + " 'y': [i[1] for i in noisy_circles[0]],\n", + " 'cluster' : noisy_circles[1]}\n", + "\n", + "df = pd.DataFrame(d)\n", + "\n", + "df = df.reindex(['x', 'y', 'cluster'], axis = 1)\n", + "#df.to_csv('blobs.csv', sep=',', encoding='utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#CREATE 3D DATASETS\n", + "\n", + "import pandas as pd\n", + "\n", + "d = {'x': [i[0] for i in noisy_circles[0]],\n", + " 'y': [i[1] for i in noisy_circles[0]],\n", + " 'cluster' : noisy_circles[1]}\n", + "\n", + "df = pd.DataFrame(d)\n", + "a = df[df['cluster'] == 0]\n", + "a['z'] = 0\n", + "\n", + "b = df[df['cluster'] == 1]\n", + "b.columns = ['cluster', 'x', 'z']\n", + "b['y'] = 0\n", + "\n", + "c = df[df['cluster'] == 2]\n", + "c.columns = ['cluster','z', 'y']\n", + "c['x'] = 0\n", + "\n", + "df = a.append(b)\n", + "df = df.append(c)\n", + "\n", + "df = df.reindex(['x', 'y', 'z', 'cluster'], axis = 1)\n", + "df\n", + "\n", + "#df.to_csv('blobs_3d.csv', sep=',', encoding='utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/jupyter/noisy_circles.csv b/jupyter/noisy_circles.csv new file mode 100644 index 0000000..eb04428 --- /dev/null +++ b/jupyter/noisy_circles.csv @@ -0,0 +1,1501 @@ +,x,y,cluster +0,-0.6779993809777912,-0.6987569823277073,0 +1,0.9314374622601281,0.19139133450454474,0 +2,0.5482913114944972,-0.006017146208598344,1 +3,0.872836923293258,0.37502331782354126,0 +4,0.43542720307989646,-0.2947263792601815,1 +5,-0.536253308256206,-0.16347080733356867,1 +6,0.9355339859673355,-0.06995023782726581,0 +7,0.1592193556542589,0.971414940769463,0 +8,-0.6115473054349307,0.7514184441469969,0 +9,0.4230403101166977,-0.26812539363169363,1 +10,0.17314659908078311,-0.4583626068786287,1 +11,-0.21912529636722505,-0.39884898968459914,1 +12,0.09610892853805891,-0.5196019320106454,1 +13,-0.10448590238211658,0.9420768517301215,0 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+1470,-0.38036459762763364,0.0,-0.34834944955016395,1 +1472,0.32530965936283257,0.0,0.44352118022030684,1 +1474,0.43263521363539453,0.0,-0.22010481193019577,1 +1475,0.5090248391825499,0.0,-0.003330775450728577,1 +1476,-0.14049132500544517,0.0,0.4589927886897831,1 +1478,0.3828928869831977,0.0,0.1809347318792307,1 +1480,0.3065910568743957,0.0,0.3142378285006997,1 +1482,0.2716232836489498,0.0,0.3210171226258232,1 +1488,0.3610017911382963,0.0,-0.34389420338605775,1 +1490,-0.41892817870059595,0.0,0.07647497554386212,1 +1493,-0.23898513554608292,0.0,0.4341012758054048,1 +1494,0.22241312217839226,0.0,-0.4050341126977784,1 +1495,0.47320535871318625,0.0,0.0341841699625143,1 +1496,0.406588044600236,0.0,0.27408916449115717,1 +1497,-0.34518815815883647,0.0,-0.35804797380344483,1 From 0562c3c0110494d891c2eac5ed9a8a2324524348 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Tue, 12 Feb 2019 14:30:05 +0100 Subject: [PATCH 05/65] added testbed also added new dataset overlap.csv modified delta calculation in estimate_delta --- jupyter/bc_utils.py | 4 +- jupyter/iris.csv | 302 ++--- jupyter/overlap.csv | 3001 +++++++++++++++++++++++++++++++++++++++++ jupyter/testbed.ipynb | 996 ++++++++++++++ 4 files changed, 4151 insertions(+), 152 deletions(-) create mode 100644 jupyter/overlap.csv create mode 100644 jupyter/testbed.ipynb diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index d2194c7..14e48ed 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -206,5 +206,7 @@ def estimate_delta(df, similarity_measure): sim = similarity_measure(point, ref_point) similarities = np.append(similarities, sim) - delta = np.median(similarities) + # aiming to get 10 representatives for the whole dataset, though picking from sorted value of similarities + # this counts on uniform distribution of values + delta = np.sort(similarities)[similarities.size / 10] return delta * 1.05 #TODO store this value somewhere better diff --git a/jupyter/iris.csv b/jupyter/iris.csv index 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"\n", + "import bc_utils as butils\n", + "\n", + "from scipy.spatial import distance\n", + "from mpl_toolkits import mplot3d\n", + "\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.preprocessing import StandardScaler \n", + "from sklearn.neighbors import KNeighborsClassifier " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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3000 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " x y z cluster\n", + "0 115 268 129 0\n", + "1 409 112 328 0\n", + "2 475 452 88 0\n", + "3 416 346 311 0\n", + "4 97 272 302 0\n", + "5 246 232 469 0\n", + "6 212 352 445 0\n", + "7 176 346 70 0\n", + "8 101 495 494 0\n", + "9 383 295 388 0\n", + "10 390 448 100 0\n", + "11 212 148 214 0\n", + "12 269 153 103 0\n", + "13 177 202 293 0\n", + "14 136 368 223 0\n", + "15 174 186 187 0\n", + "16 406 363 304 0\n", + "17 69 184 451 0\n", + "18 140 212 97 0\n", + "19 172 59 134 0\n", + "20 60 122 353 0\n", + "21 235 353 103 0\n", + "22 418 279 74 0\n", + "23 208 474 385 0\n", + "24 146 138 148 0\n", + "25 452 423 130 0\n", + "26 248 336 315 0\n", + "27 93 322 405 0\n", + "28 372 370 114 0\n", + "29 223 350 139 0\n", + "... ... ... ... ...\n", + "2970 395 515 376 5\n", + "2971 405 231 739 5\n", + "2972 597 342 423 5\n", + "2973 424 480 794 5\n", + "2974 646 498 673 5\n", + "2975 701 536 512 5\n", + "2976 652 443 700 5\n", + "2977 770 223 510 5\n", + "2978 723 120 501 5\n", + "2979 482 235 537 5\n", + "2980 626 239 396 5\n", + "2981 434 281 519 5\n", + "2982 477 380 489 5\n", + "2983 753 509 670 5\n", + "2984 788 135 600 5\n", + "2985 504 338 425 5\n", + "2986 567 315 694 5\n", + "2987 663 218 742 5\n", + "2988 624 525 430 5\n", + "2989 639 168 385 5\n", + "2990 609 414 646 5\n", + "2991 549 447 555 5\n", + "2992 403 306 542 5\n", + "2993 397 151 719 5\n", + "2994 552 135 496 5\n", + "2995 649 202 655 5\n", + "2996 607 375 477 5\n", + "2997 489 532 480 5\n", + "2998 763 350 645 5\n", + "2999 357 510 698 5\n", + "\n", + "[3000 rows x 4 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read dataset to pandas dataframe\n", + "# current datasets ready for testing:\n", + "# blobs.csv\n", + "# blobs_3d.csv\n", + "# iris.csv\n", + "# noisy_circles.csv\n", + "# noisy_circles_3d.csv\n", + "# overlap.csv\n", + "\n", + "df = pd.read_csv(\"overlap.csv\", index_col=0)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Plot data\n", + "\n", + "Visualize data if they have 2 or 3 features." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "colors = {0:'red', 1:'blue', 2:'green'}\n", + "\n", + "if (df.columns.size == 3):\n", + " fix, ax = plt.subplots()\n", + " grouped = df.groupby('cluster')\n", + " \n", + " i = 0\n", + " for group in grouped:\n", + " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", + " label=key, color=colors[i])\n", + " i = i + 1\n", + " plt.show()\n", + "elif (df.columns.size == 4):\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(111, projection='3d')\n", + " grouped = df.groupby('cluster')\n", + "\n", + " i = 0\n", + " for key, group in grouped:\n", + " ax.scatter(group['x'], group['y'], group['z'], color=colors[i])\n", + " i = i + 1\n", + " plt.show()\n", + "else:\n", + " print(\"There is no good visualisation for this dataset - \" + str(df.columns.size) + \" features.\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#creating test datasets for iris\n", + "names = set(df['cluster'])\n", + "\n", + "dataframes = {}\n", + "for name in names:\n", + " tmp_df = df[df['cluster'] == name]\n", + " dataframes[name] = butils.TestDf(tmp_df)\n", + " \n", + "full_test_df = pd.DataFrame()\n", + "for name in names:\n", + " full_test_df = full_test_df.append(dataframes[name].test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "delta_medoids_full algorithm ended after 5 iterations.\n", + "delta_medoids_full algorithm ended after 6 iterations.\n", + "delta_medoids_full algorithm ended after 5 iterations.\n", + "{0: x y z cluster\n", + "0 425 162 496 0\n", + "1 75 144 269 0\n", + "2 416 346 311 0\n", + "3 264 450 417 0\n", + "4 265 92 95 0\n", + "5 77 377 97 0\n", + "6 442 129 134 0\n", + "7 132 108 100 0\n", + "8 248 336 315 0\n", + "9 376 260 118 0\n", + "10 126 268 132 0\n", + "11 69 184 451 0\n", + "12 401 323 446 0\n", + "13 243 302 114 0\n", + "14 452 423 130 0\n", + "15 409 112 328 0\n", + "16 177 436 127 0\n", + "17 248 273 438 0\n", + "18 100 295 304 0\n", + "19 212 148 214 0\n", + "20 209 98 397 0\n", + "21 114 435 470 0\n", + "22 195 482 291 0\n", + "23 407 414 378 0, 3: x y z cluster\n", + "0 282 535 514 3\n", + "1 352 770 896 3\n", + "2 328 759 574 3\n", + "3 517 850 889 3\n", + "4 323 859 875 3\n", + "5 213 690 547 3\n", + "6 317 898 633 3\n", + "7 537 492 661 3\n", + "8 301 483 743 3\n", + "9 439 624 719 3\n", + "10 487 707 594 3\n", + "11 486 629 883 3\n", + "12 393 768 757 3\n", + "13 258 855 770 3\n", + "14 533 532 521 3\n", + "15 437 509 820 3\n", + "16 222 905 531 3\n", + "17 589 549 846 3\n", + "18 293 636 793 3\n", + "19 578 890 775 3\n", + "20 606 560 635 3\n", + "21 562 726 751 3\n", + "22 389 904 521 3\n", + "23 252 566 673 3\n", + "24 247 786 828 3\n", + "25 280 541 859 3\n", + "26 597 796 562 3\n", + "27 469 862 647 3\n", + "28 639 705 894 3\n", + "29 365 590 564 3, 5: x y z cluster\n", + "0 615 187 727 5\n", + "1 654 324 512 5\n", + "2 781 531 466 5\n", + "3 719 140 475 5\n", + "4 452 371 416 5\n", + "5 538 173 474 5\n", + "6 373 164 383 5\n", + "7 763 498 728 5\n", + "8 440 405 621 5\n", + "9 751 306 710 5\n", + "10 618 488 419 5\n", + "11 671 490 573 5\n", + "12 395 510 764 5\n", + "13 388 264 491 5\n", + "14 434 345 718 5\n", + "15 708 267 398 5\n", + "16 407 527 460 5\n", + "17 443 184 659 5\n", + "18 535 423 722 5}\n" + ] + } + ], + "source": [ + "#using delta_medoids_full algorithm\n", + "results = {}\n", + "for name in names:\n", + " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", + "\n", + " result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", + " result['cluster'] = name\n", + " results[name] = result\n", + " \n", + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: x y z cluster\n", + "0 115 268 129 0\n", + "1 409 112 328 0\n", + "2 475 452 88 0\n", + "3 416 346 311 0\n", + "4 97 272 302 0\n", + "5 246 232 469 0\n", + "6 101 495 494 0\n", + "7 212 148 214 0\n", + "8 69 184 451 0\n", + "9 418 279 74 0\n", + "10 248 336 315 0\n", + "11 263 301 67 0\n", + "12 196 463 110 0\n", + "13 425 162 496 0\n", + "14 489 164 181 0\n", + "15 55 89 226 0\n", + "16 429 478 395 0\n", + "17 197 491 264 0\n", + "18 51 390 53 0\n", + "19 209 98 397 0\n", + "20 283 55 102 0\n", + "21 280 468 456 0\n", + "22 393 320 493 0\n", + "23 88 91 50 0, 3: x y z cluster\n", + "0 605 798 583 3\n", + "1 541 735 733 3\n", + "2 486 629 883 3\n", + "3 644 586 626 3\n", + "4 317 898 633 3\n", + "5 319 884 888 3\n", + "6 264 628 828 3\n", + "7 320 761 552 3\n", + "8 365 590 564 3\n", + "9 641 542 839 3\n", + "10 219 557 639 3\n", + "11 393 768 757 3\n", + "12 272 486 501 3\n", + "13 418 496 839 3\n", + "14 542 831 906 3\n", + "15 487 707 594 3\n", + "16 600 523 484 3\n", + "17 581 911 746 3\n", + "18 243 486 890 3\n", + "19 423 886 482 3\n", + "20 204 772 863 3\n", + "21 379 749 918 3\n", + "22 523 487 672 3\n", + "23 201 899 528 3\n", + "24 218 643 495 3\n", + "25 301 483 743 3\n", + "26 639 705 894 3\n", + "27 469 862 647 3\n", + "28 229 889 760 3\n", + "29 439 624 719 3, 5: x y z cluster\n", + "0 443 184 659 5\n", + "1 533 143 453 5\n", + "2 421 412 601 5\n", + "3 768 542 791 5\n", + "4 567 430 791 5\n", + "5 769 283 363 5\n", + "6 665 506 569 5\n", + "7 798 328 755 5\n", + "8 797 544 410 5\n", + "9 576 511 374 5\n", + "10 630 181 748 5\n", + "11 630 305 547 5\n", + "12 388 536 441 5\n", + "13 388 526 748 5\n", + "14 350 265 523 5\n", + "15 452 371 416 5\n", + "16 711 123 521 5\n", + "17 358 111 368 5\n", + "18 367 333 785 5}\n" + ] + } + ], + "source": [ + "#using delta_medoids_one_shot algorithm\n", + "results2 = {}\n", + "for name in names:\n", + " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", + "\n", + " result = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", + " result['cluster'] = name\n", + " results2[name] = result\n", + " \n", + "print(results2)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "#creating training DataFrames for comparing oneshot and full delta medoids algorithm\n", + "train_delta_medoids_full = pd.DataFrame()\n", + "train_delta_medoids_one_shot = pd.DataFrame()\n", + "\n", + "for name in names:\n", + " train_delta_medoids_full = train_delta_medoids_full.append(results[name])\n", + " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(results2[name])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "def classifyPoints(ref_df, test_df):\n", + "\n", + " X_train = ref_df.iloc[:, :-1].values\n", + " y_train = ref_df.iloc[:, -1].values\n", + " X_test = test_df.iloc[:, :-1].values\n", + " y_test = test_df.iloc[:, -1].values\n", + "\n", + " from sklearn.preprocessing import StandardScaler \n", + " scaler = StandardScaler() \n", + " scaler.fit(X_train)\n", + "\n", + " X_train = scaler.transform(X_train) \n", + " X_test = scaler.transform(X_test)\n", + "\n", + " from sklearn.neighbors import KNeighborsClassifier \n", + " classifier = KNeighborsClassifier(n_neighbors=5)\n", + " classifier.fit(X_train, y_train)\n", + "\n", + " y_pred = classifier.predict(X_test)\n", + "\n", + " from sklearn.metrics import classification_report, confusion_matrix \n", + " print(confusion_matrix(y_test, y_pred)) \n", + " print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[190 0 10]\n", + " [ 4 234 2]\n", + " [ 12 13 135]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.92 0.95 0.94 200\n", + " 3 0.95 0.97 0.96 240\n", + " 5 0.92 0.84 0.88 160\n", + "\n", + " micro avg 0.93 0.93 0.93 600\n", + " macro avg 0.93 0.92 0.93 600\n", + "weighted avg 0.93 0.93 0.93 600\n", + "\n" + ] + } + ], + "source": [ + "classifyPoints(train_delta_medoids_full, full_test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[189 1 10]\n", + " [ 3 234 3]\n", + " [ 16 17 127]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.91 0.94 0.93 200\n", + " 3 0.93 0.97 0.95 240\n", + " 5 0.91 0.79 0.85 160\n", + "\n", + " micro avg 0.92 0.92 0.92 600\n", + " macro avg 0.91 0.90 0.91 600\n", + "weighted avg 0.92 0.92 0.92 600\n", + "\n" + ] + } + ], + "source": [ + "classifyPoints(train_delta_medoids_one_shot, full_test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 825e9005f50e0b01f795725be295ab7ffa8fe342 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Thu, 14 Feb 2019 13:27:50 +0100 Subject: [PATCH 06/65] bugfix: delta_medoids_full not updating cluster keys added pendigit.csv dataset --- jupyter/bc_utils.py | 5 +- jupyter/pendigit.csv | 3498 +++++++++++++++++++++++++++++++++++++++++ jupyter/testbed.ipynb | 802 +--------- 3 files changed, 3531 insertions(+), 774 deletions(-) create mode 100644 jupyter/pendigit.csv diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index 14e48ed..09b6b0e 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -145,10 +145,13 @@ def delta_medoids_full(df, delta, similarity_measure): t = 0 representatives = {} representatives[t] = set() - clusters = {} while True: #print("\n=========== running for t = " + str(t) + "============") + clusters = {} + for item in representatives[t]: + clusters[item] = np.array(item, ndmin=2) + t = t + 1 #================== RepAssign starts ================== diff --git a/jupyter/pendigit.csv b/jupyter/pendigit.csv new file mode 100644 index 0000000..d1c3939 --- /dev/null +++ b/jupyter/pendigit.csv @@ -0,0 +1,3498 @@ +,a,b,c,d,e,f,g,h,i,j,l,m,n,o,p,q,cluster +0,80,100,18,98,60,66,100,29,42,0,0,23,42,61,56,98,8 +1,0,94,9,57,20,19,7,0,20,36,70,68,100,100,18,92,8 +2,95,82,71,100,27,77,77,73,100,80,93,42,56,13,0,0,9 +3,68,100,6,88,47,75,87,82,85,56,100,29,75,6,0,0,9 +4,70,100,100,97,70,81,45,65,30,49,20,33,0,16,0,0,1 +5,40,100,0,81,15,58,100,57,47,87,50,88,40,42,36,0,4 +6,3,71,0,95,45,100,100,99,79,78,48,53,31,24,54,0,7 +7,79,87,98,81,71,100,72,73,100,66,91,21,48,0,0,13,9 +8,92,95,30,100,34,68,87,89,84,78,100,35,64,0,0,19,9 +9,58,64,100,96,27,100,0,63,79,65,91,72,48,36,10,0,9 +10,34,89,3,70,1,25,49,0,100,23,100,67,56,99,0,100,0 +11,0,90,46,100,88,92,79,69,60,48,39,27,47,6,100,0,2 +12,20,71,0,29,31,0,78,12,100,51,84,93,37,100,8,66,0 +13,100,100,67,98,41,80,44,50,78,42,68,16,35,2,0,0,5 +14,91,69,48,57,9,79,60,100,100,75,95,40,64,8,0,0,9 +15,30,74,55,100,89,87,66,56,100,38,92,8,41,0,0,20,3 +16,5,65,0,89,37,100,88,97,100,79,71,53,48,26,59,0,7 +17,42,93,19,88,0,42,24,0,83,11,100,56,75,100,17,97,0 +18,4,100,0,72,15,44,79,50,100,76,90,51,83,22,85,0,4 +19,100,100,79,80,54,59,32,38,21,16,50,0,79,16,0,16,6 +20,0,76,30,48,53,9,11,0,47,34,97,66,100,100,38,85,8 +21,0,93,40,100,57,61,51,17,35,0,12,37,54,48,100,49,7 +22,100,100,73,95,40,69,14,39,2,2,39,0,37,29,0,14,6 +23,88,100,42,77,12,50,0,22,45,0,100,17,73,44,15,49,6 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index 62d9ad1..530a09d 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -30,543 +30,11 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
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3000 rows × 4 columns

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pd.read_csv(\"pendigit.csv\", index_col=0)\n", "df" ] }, @@ -592,24 +61,11 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "colors = {0:'red', 1:'blue', 2:'green'}\n", "\n", @@ -640,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": { "scrolled": true }, @@ -661,208 +117,46 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "delta_medoids_full algorithm ended after 5 iterations.\n", - "delta_medoids_full algorithm ended after 6 iterations.\n", - "delta_medoids_full algorithm ended after 5 iterations.\n", - "{0: x y z cluster\n", - "0 425 162 496 0\n", - "1 75 144 269 0\n", - "2 416 346 311 0\n", - "3 264 450 417 0\n", - "4 265 92 95 0\n", - "5 77 377 97 0\n", - "6 442 129 134 0\n", - "7 132 108 100 0\n", - "8 248 336 315 0\n", - "9 376 260 118 0\n", - "10 126 268 132 0\n", - "11 69 184 451 0\n", - "12 401 323 446 0\n", - "13 243 302 114 0\n", - "14 452 423 130 0\n", - "15 409 112 328 0\n", - "16 177 436 127 0\n", - "17 248 273 438 0\n", - "18 100 295 304 0\n", - "19 212 148 214 0\n", - "20 209 98 397 0\n", - "21 114 435 470 0\n", - "22 195 482 291 0\n", - "23 407 414 378 0, 3: x y z cluster\n", - "0 282 535 514 3\n", - "1 352 770 896 3\n", - "2 328 759 574 3\n", - "3 517 850 889 3\n", - "4 323 859 875 3\n", - "5 213 690 547 3\n", - "6 317 898 633 3\n", - "7 537 492 661 3\n", - "8 301 483 743 3\n", - "9 439 624 719 3\n", - "10 487 707 594 3\n", - "11 486 629 883 3\n", - "12 393 768 757 3\n", - "13 258 855 770 3\n", - "14 533 532 521 3\n", - "15 437 509 820 3\n", - "16 222 905 531 3\n", - "17 589 549 846 3\n", - "18 293 636 793 3\n", - "19 578 890 775 3\n", - "20 606 560 635 3\n", - "21 562 726 751 3\n", - "22 389 904 521 3\n", - "23 252 566 673 3\n", - "24 247 786 828 3\n", - "25 280 541 859 3\n", - "26 597 796 562 3\n", - "27 469 862 647 3\n", - "28 639 705 894 3\n", - "29 365 590 564 3, 5: x y z cluster\n", - "0 615 187 727 5\n", - "1 654 324 512 5\n", - "2 781 531 466 5\n", - "3 719 140 475 5\n", - "4 452 371 416 5\n", - "5 538 173 474 5\n", - "6 373 164 383 5\n", - "7 763 498 728 5\n", - "8 440 405 621 5\n", - "9 751 306 710 5\n", - "10 618 488 419 5\n", - "11 671 490 573 5\n", - "12 395 510 764 5\n", - "13 388 264 491 5\n", - "14 434 345 718 5\n", - "15 708 267 398 5\n", - "16 407 527 460 5\n", - "17 443 184 659 5\n", - "18 535 423 722 5}\n" - ] - } - ], + "outputs": [], "source": [ "#using delta_medoids_full algorithm\n", "results = {}\n", + "res_len = []\n", "for name in names:\n", " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", "\n", - " result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", + " result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.euclidean) , distance.euclidean)\n", " result['cluster'] = name\n", " results[name] = result\n", - " \n", - "print(results)" + " res_len.append(result.shape[0])\n", + "\n", + "print(res_len)" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{0: x y z cluster\n", - "0 115 268 129 0\n", - "1 409 112 328 0\n", - "2 475 452 88 0\n", - "3 416 346 311 0\n", - "4 97 272 302 0\n", - "5 246 232 469 0\n", - "6 101 495 494 0\n", - "7 212 148 214 0\n", - "8 69 184 451 0\n", - "9 418 279 74 0\n", - "10 248 336 315 0\n", - "11 263 301 67 0\n", - "12 196 463 110 0\n", - "13 425 162 496 0\n", - "14 489 164 181 0\n", - "15 55 89 226 0\n", - "16 429 478 395 0\n", - "17 197 491 264 0\n", - "18 51 390 53 0\n", - "19 209 98 397 0\n", - "20 283 55 102 0\n", - "21 280 468 456 0\n", - "22 393 320 493 0\n", - "23 88 91 50 0, 3: x y z cluster\n", - "0 605 798 583 3\n", - "1 541 735 733 3\n", - "2 486 629 883 3\n", - "3 644 586 626 3\n", - "4 317 898 633 3\n", - "5 319 884 888 3\n", - "6 264 628 828 3\n", - "7 320 761 552 3\n", - "8 365 590 564 3\n", - "9 641 542 839 3\n", - "10 219 557 639 3\n", - "11 393 768 757 3\n", - "12 272 486 501 3\n", - "13 418 496 839 3\n", - "14 542 831 906 3\n", - "15 487 707 594 3\n", - "16 600 523 484 3\n", - "17 581 911 746 3\n", - "18 243 486 890 3\n", - "19 423 886 482 3\n", - "20 204 772 863 3\n", - "21 379 749 918 3\n", - "22 523 487 672 3\n", - "23 201 899 528 3\n", - "24 218 643 495 3\n", - "25 301 483 743 3\n", - "26 639 705 894 3\n", - "27 469 862 647 3\n", - "28 229 889 760 3\n", - "29 439 624 719 3, 5: x y z cluster\n", - "0 443 184 659 5\n", - "1 533 143 453 5\n", - "2 421 412 601 5\n", - "3 768 542 791 5\n", - "4 567 430 791 5\n", - "5 769 283 363 5\n", - "6 665 506 569 5\n", - "7 798 328 755 5\n", - "8 797 544 410 5\n", - "9 576 511 374 5\n", - "10 630 181 748 5\n", - "11 630 305 547 5\n", - "12 388 536 441 5\n", - "13 388 526 748 5\n", - "14 350 265 523 5\n", - "15 452 371 416 5\n", - "16 711 123 521 5\n", - "17 358 111 368 5\n", - "18 367 333 785 5}\n" - ] - } - ], + "outputs": [], "source": [ "#using delta_medoids_one_shot algorithm\n", "results2 = {}\n", + "res_2_len = []\n", "for name in names:\n", " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", "\n", " result = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", " result['cluster'] = name\n", " results2[name] = result\n", - " \n", - "print(results2)" + " res_2_len.append(result.shape[0])\n", + "print(res_2_len)" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -877,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -908,58 +202,20 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[190 0 10]\n", - " [ 4 234 2]\n", - " [ 12 13 135]]\n", - " precision recall f1-score support\n", - "\n", - " 0 0.92 0.95 0.94 200\n", - " 3 0.95 0.97 0.96 240\n", - " 5 0.92 0.84 0.88 160\n", - "\n", - " micro avg 0.93 0.93 0.93 600\n", - " macro avg 0.93 0.92 0.93 600\n", - "weighted avg 0.93 0.93 0.93 600\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "classifyPoints(train_delta_medoids_full, full_test_df)" ] }, { "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[189 1 10]\n", - " [ 3 234 3]\n", - " [ 16 17 127]]\n", - " precision recall f1-score support\n", - "\n", - " 0 0.91 0.94 0.93 200\n", - " 3 0.93 0.97 0.95 240\n", - " 5 0.91 0.79 0.85 160\n", - "\n", - " micro avg 0.92 0.92 0.92 600\n", - " macro avg 0.91 0.90 0.91 600\n", - "weighted avg 0.92 0.92 0.92 600\n", - "\n" - ] - } - ], + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], "source": [ "classifyPoints(train_delta_medoids_one_shot, full_test_df)" ] From 9be61f5cd1ea3e9ba70bc1c97600d5addd87a7b6 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 17 Feb 2019 21:57:27 +0100 Subject: [PATCH 07/65] added new datasets and better plotting of results matrices returned by classify points method are now displayed in a tile plot --- jupyter/{ => data}/blobs.csv | 0 jupyter/{ => data}/blobs_3d.csv | 0 jupyter/{ => data}/iris.csv | 0 jupyter/data/mfeat-mor.csv | 2001 +++++++++++++++++++++++ jupyter/data/mfeat-zer.csv | 2001 +++++++++++++++++++++++ jupyter/{ => data}/noisy_circles.csv | 0 jupyter/{ => data}/noisy_circles_3d.csv | 0 jupyter/{ => data}/overlap.csv | 0 jupyter/{ => data}/pendigit.csv | 0 jupyter/testbed.ipynb | 66 +- 10 files changed, 4062 insertions(+), 6 deletions(-) rename jupyter/{ => data}/blobs.csv (100%) rename jupyter/{ => data}/blobs_3d.csv (100%) rename jupyter/{ => data}/iris.csv (100%) create mode 100644 jupyter/data/mfeat-mor.csv create mode 100644 jupyter/data/mfeat-zer.csv rename jupyter/{ => data}/noisy_circles.csv (100%) rename jupyter/{ => data}/noisy_circles_3d.csv (100%) rename jupyter/{ => data}/overlap.csv (100%) rename jupyter/{ => data}/pendigit.csv (100%) diff --git a/jupyter/blobs.csv b/jupyter/data/blobs.csv similarity index 100% rename from jupyter/blobs.csv rename to jupyter/data/blobs.csv diff --git a/jupyter/blobs_3d.csv b/jupyter/data/blobs_3d.csv similarity index 100% rename from jupyter/blobs_3d.csv rename to jupyter/data/blobs_3d.csv diff --git a/jupyter/iris.csv b/jupyter/data/iris.csv similarity index 100% rename from jupyter/iris.csv rename to jupyter/data/iris.csv diff --git a/jupyter/data/mfeat-mor.csv b/jupyter/data/mfeat-mor.csv new file mode 100644 index 0000000..584b4a3 --- /dev/null +++ b/jupyter/data/mfeat-mor.csv @@ -0,0 +1,2001 @@ +,a,b,c,d,e,f,cluster +0,1.0,0.0,0.0,133.1508614,1.31169276,1620.22177892,zero +1,1.0,0.0,0.0,126.7248614,1.30274497,1609.33482208,zero 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+1999,0.02974044,0.77684359,9.82989695,40.58712217,51.18813712,148.02076965,124.93928208,0.08330161,2.78035367,30.0290158,119.16031597,186.71913433,153.38842005,0.09344144,2.71863798,19.8948077,34.9368113,25.57463095,37.59700698,0.34545983,8.71033767,56.26971407,86.91766538,157.33084084,0.36711254,6.29816205,40.8237322,146.84018578,15.24207123,1.24199088,17.81449871,17.22160878,134.29869966,0.97959074,21.98986888,215.95873639,112.32511052,2.84996075,6.11719618,182.71687575,5.03125108,148.44774937,336.81851415,3.12548098,160.04524347,43.65382382,408.1714145,9 diff --git a/jupyter/noisy_circles.csv b/jupyter/data/noisy_circles.csv similarity index 100% rename from jupyter/noisy_circles.csv rename to jupyter/data/noisy_circles.csv diff --git a/jupyter/noisy_circles_3d.csv b/jupyter/data/noisy_circles_3d.csv similarity index 100% rename from jupyter/noisy_circles_3d.csv rename to jupyter/data/noisy_circles_3d.csv diff --git a/jupyter/overlap.csv b/jupyter/data/overlap.csv similarity index 100% rename from jupyter/overlap.csv rename to jupyter/data/overlap.csv diff --git a/jupyter/pendigit.csv b/jupyter/data/pendigit.csv similarity index 100% rename from jupyter/pendigit.csv rename to jupyter/data/pendigit.csv diff --git a/jupyter/testbed.ipynb b/jupyter/testbed.ipynb index 530a09d..d93f03f 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -41,12 +41,14 @@ "# blobs.csv\n", "# blobs_3d.csv\n", "# iris.csv\n", + "# mfeat-mor.csv\n", + "# mfeat-zer.csv\n", "# noisy_circles.csv\n", "# noisy_circles_3d.csv\n", "# overlap.csv\n", "# pendigit.csv\n", "\n", - "df = pd.read_csv(\"pendigit.csv\", index_col=0)\n", + "df = pd.read_csv(\"data/pendigit.csv\", index_col=0)\n", "df" ] }, @@ -195,9 +197,11 @@ "\n", " y_pred = classifier.predict(X_test)\n", "\n", - " from sklearn.metrics import classification_report, confusion_matrix \n", - " print(confusion_matrix(y_test, y_pred)) \n", - " print(classification_report(y_test, y_pred))" + " from sklearn.metrics import classification_report, confusion_matrix\n", + " conf_mat = confusion_matrix(y_test, y_pred)\n", + " print(conf_mat) \n", + " print(classification_report(y_test, y_pred))\n", + " return conf_mat" ] }, { @@ -206,7 +210,7 @@ "metadata": {}, "outputs": [], "source": [ - "classifyPoints(train_delta_medoids_full, full_test_df)" + "matrix_full = classifyPoints(train_delta_medoids_full, full_test_df)" ] }, { @@ -217,7 +221,57 @@ }, "outputs": [], "source": [ - "classifyPoints(train_delta_medoids_one_shot, full_test_df)" + "matrix_one_shot = classifyPoints(train_delta_medoids_one_shot, full_test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def benchmark_result_plots(matrices, titles, headers):\n", + " \"\"\"\n", + " parameters:\n", + " matrices - list of 2d np.array\n", + " titles - list of graph titles\n", + " names - list of labels\n", + " \"\"\"\n", + " fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(15,15))\n", + " ax_list = [ax1, ax2, ax3, ax4]\n", + "\n", + " for index in range(0, len(matrices)):\n", + " tick_labels = sorted(list(headers[index]))\n", + " ax = ax_list[index]\n", + " ax.imshow(matrices[index], cmap='binary')\n", + "\n", + " # We want to show all ticks...\n", + " ax.set_xticks(np.arange(len(tick_labels)))\n", + " ax.set_yticks(np.arange(len(tick_labels)))\n", + "\n", + " # ... and label them with the respective list entries\n", + " ax.set_xticklabels(tick_labels)\n", + " ax.set_yticklabels(tick_labels)\n", + "\n", + " # Rotate the tick labels and set their alignment.\n", + " plt.setp(ax.get_xticklabels(), ha=\"right\")\n", + "\n", + " # Loop over data dimensions and create text annotations.\n", + " for i in range(len(tick_labels)):\n", + " for j in range(len(tick_labels)):\n", + " if(matrices[index][i, j] == 0):\n", + " continue\n", + " if i == j:\n", + " text = ax.text(j, i, matrices[index][i, j], ha=\"center\", va=\"center\", color=\"g\",\n", + " weight=\"bold\", fontsize=16)\n", + " continue\n", + " else:\n", + " text = ax.text(j, i, matrices[index][i, j], ha=\"center\", va=\"center\", color = \"r\",\n", + " weight=\"bold\", fontsize=16)\n", + " \n", + " ax.set_title(titles[index])\n", + " \n", + "benchmark_result_plots([matrix_full, matrix_one_shot], ['Delta-Medoids Full', 'Delta-Medoids One Shot'], [names, names])" ] }, { From be9e24bc19638b726b963f1c797a440e658ef10d Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 17 Feb 2019 22:00:29 +0100 Subject: [PATCH 08/65] updated old files --- data-generation/datagen.py | 89 ++++++++++++++------------------------ datasets/overlap.csv | 2 +- 2 files changed, 34 insertions(+), 57 deletions(-) diff --git a/data-generation/datagen.py b/data-generation/datagen.py index 0ca1163..4af3f6e 100644 --- a/data-generation/datagen.py +++ b/data-generation/datagen.py @@ -1,66 +1,43 @@ +import json from random import randint -import csv -import sys #------------------------------------------------------------------------- -# Generates data in 3d space 0 to 1000 cube. -# Outputs a csv file in format cluster number, x, y, z -# Parameters: -# cluster_size integer - number of points to generate in each cluster -# cluster_number integer - number of clusters in space, capped at 8 -# file_name string - name of file to save the cluster -# noise boolean - True - generate only clusters, False - generate some more random points -def generate_3d_data(file_name, cluster_number, cluster_size, noise): - - if (cluster_number) > 8: - return False - - with open(file_name, 'w', newline='') as csv_file: - data_writer = csv.writer(csv_file, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL) - for cluster in range(0, cluster_number): - x = 100 + 500 * ((cluster >> 2) % 2) - y = 100 + 500 * ((cluster >> 1) % 2) - z = 100 + 500 * ((cluster >> 0) % 2) - for item in range(1, cluster_size + 1): - data_writer.writerow([cluster, randint(0, 450) + x, randint(0, 450) - + y, randint(0, 450) + z]) - if noise == True: - generate_random_points(cluster_size, file_name) - return True - -#------------------------------------------------------------------------- -# Generates random points in 0 to 1000 space and appends them to csv file -# Format 100, x, y, z where 100 stands for no cluster but scattered random points -# Parameters: -# number_of_points integer - how many points to generate -# file_name string - to what file to append -def generate_random_points(number_of_points, file_name): - with open(file_name, 'a', newline='') as csv_file: - data_writer = csv.writer(csv_file, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL) - for i in range(0, number_of_points + 1): - data_writer.writerow([100, randint(0, 1000), randint(0, 1000), randint(0, 1000)]) +# filling table = for size = 4 +# XX | a0 | a1 | a2 | a3 +# ------------------------ +# a0 | XX | | | +# ------------------------ +# a1 |fill| XX | | +# ------------------------ +# a2 |fill|fill| XX | +# ------------------------ +# a3 |fill|fill|fill| XX +# ------------------------ +def generate_similarity_tables(size): + + ret_dict = {} + id_num = 0 + while id_num != size: #generate identificators + ret_dict.update({"a" + str(id_num) : []}) + id_num += 1 + + for row in ret_dict: + for column in ret_dict: + if(row == column): + continue + app = randint(0,1000) + if (app > 800): + ret_dict[row].append({column:app}) + ret_dict[column].append({row:app}) + return ret_dict #------------------------------------------------------------------------- -print("Data generation started:") +print("Starting to generate data") -if len(sys.argv) != 5: - print("Usage: python3 datagen.py ") - exit(1) +data = generate_similarity_tables(1000) -out_file = sys.argv[1] -number_of_clusters = int(sys.argv[2]) -points_per_cluster = int(sys.argv[3]) -if (sys.argv[4] == 'T'): - gen_noise = True -elif (sys.argv[4] == 'F'): - gen_noise = False -else: - print("Please use T (true) or F (false) to generate specify whether you want to generate noise.") - exit(1) -if generate_3d_data(out_file, number_of_clusters, points_per_cluster, gen_noise): - print("Data generated.") -else: - print("Data generation failed.") +output_file = open("data.json", 'w') +output_file.write(json.dumps(data)) diff --git a/datasets/overlap.csv b/datasets/overlap.csv index 559ef73..a26b9f8 100644 --- a/datasets/overlap.csv +++ b/datasets/overlap.csv @@ -2997,4 +2997,4 @@ 5,607,375,477 5,489,532,480 5,763,350,645 -5,357,510,698 \ No newline at end of file +5,357,510,698 From 7b1dd2465d4068abadc3ee3b0fe53fbe697ab83d Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Tue, 19 Feb 2019 13:28:11 +0100 Subject: [PATCH 09/65] added random_selection algorithm --- jupyter/bc_utils.py | 7 ++++++ jupyter/testbed.ipynb | 52 ++++++++++++++++++++++++++++++++++--------- 2 files changed, 49 insertions(+), 10 deletions(-) diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index 09b6b0e..54da5e2 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -1,5 +1,6 @@ import pandas as pd import numpy as np +import random from scipy.spatial import distance from matplotlib import pyplot @@ -213,3 +214,9 @@ def estimate_delta(df, similarity_measure): # this counts on uniform distribution of values delta = np.sort(similarities)[similarities.size / 10] return delta * 1.05 #TODO store this value somewhere better + +#using random select algorithm +def random_selection(df, delta, similarity_measure, num): + rows = random.sample(df.index, num) + representatives = df.loc[rows] + return representatives diff --git a/jupyter/testbed.ipynb b/jupyter/testbed.ipynb index d93f03f..4d0ad2c 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -48,7 +48,7 @@ "# overlap.csv\n", "# pendigit.csv\n", "\n", - "df = pd.read_csv(\"data/pendigit.csv\", index_col=0)\n", + "df = pd.read_csv(\"data/noisy_circles_3d.csv\", index_col=0)\n", "df" ] }, @@ -156,6 +156,33 @@ "print(res_2_len)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#using random select algorithm \n", + "results3 = {}\n", + "res_3_len = []\n", + "for name in names:\n", + " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", + " result = butils.random_selection(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean, 10)\n", + " result['cluster'] = name\n", + " results3[name] = result\n", + " res_3_len.append(result.shape[0])\n", + "print(res_3_len)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#using greedy algorithm" + ] + }, { "cell_type": "code", "execution_count": null, @@ -165,10 +192,13 @@ "#creating training DataFrames for comparing oneshot and full delta medoids algorithm\n", "train_delta_medoids_full = pd.DataFrame()\n", "train_delta_medoids_one_shot = pd.DataFrame()\n", + "train_random_selection = pd.DataFrame()\n", "\n", "for name in names:\n", " train_delta_medoids_full = train_delta_medoids_full.append(results[name])\n", - " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(results2[name])\n" + " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(results2[name])\n", + " train_random_selection = train_random_selection.append(results3[name])\n", + " " ] }, { @@ -224,6 +254,15 @@ "matrix_one_shot = classifyPoints(train_delta_medoids_one_shot, full_test_df)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matrix_random_selection = classifyPoints(train_random_selection, full_test_df)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -271,15 +310,8 @@ " \n", " ax.set_title(titles[index])\n", " \n", - "benchmark_result_plots([matrix_full, matrix_one_shot], ['Delta-Medoids Full', 'Delta-Medoids One Shot'], [names, names])" + "benchmark_result_plots([matrix_full, matrix_one_shot, matrix_random_selection], ['Delta-Medoids Full', 'Delta-Medoids One Shot', 'Random Selection'], [names, names, names])" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From c7e6c41e93b8a3a7745bbae7ed7387f8f1543ef5 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 1 Mar 2019 11:13:15 +0100 Subject: [PATCH 10/65] moved greedy algorithm to bc_utils, improved random --- jupyter/bc_utils.py | 44 +- jupyter/data/mfeat-mor.csv | 4000 +++++++++++++++++------------------ jupyter/delta-medoids.ipynb | 124 -- jupyter/testbed.ipynb | 53 +- 4 files changed, 2086 insertions(+), 2135 deletions(-) delete mode 100644 jupyter/delta-medoids.ipynb diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index 54da5e2..8b23eae 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -216,7 +216,49 @@ def estimate_delta(df, similarity_measure): return delta * 1.05 #TODO store this value somewhere better #using random select algorithm -def random_selection(df, delta, similarity_measure, num): +def random_selection(df, delta, num): rows = random.sample(df.index, num) representatives = df.loc[rows] return representatives + +# finding representation of a cluster with random selection +# pandas.DataFrame in_df +# float delta +def greedy_select(df, delta, similarity_measure): + + medoid_indexes = np.array([]) + index_list = np.array(df.index.values) + + while (index_list.size > 0): + + # selecting the first index + if not (medoid_indexes.size > 0): + current_index = df.loc[index_list[0]].name + # finding index of the most different point + else: + for index in index_list: + current_index = None + diff_sum = 0 + tmp_sum = 0 + for medoid in medoid_indexes: + tmp_sum = tmp_sum + similarity_measure(df.loc[index].values[:], + df.loc[medoid].values[:]) + if tmp_sum > diff_sum: + diff_sum = tmp_sum + current_index = index + + rem_indexes = np.array([]) + + # find indexes to drop from dataframe + for ix in df.index: + if 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pyplot\n", - "\n", - "import bc_utils as butils\n", - "from sklearn.datasets.samples_generator import make_blobs\n", - "from scipy.spatial import distance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating a test cluster:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X, y = make_blobs(n_samples=300, centers=2, cluster_std=1.9, n_features=5)\n", - "df = pd.DataFrame(dict(a=X[:,0], b=X[:,1],c=X[:,2], d=X[:,3],e=X[:,4]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting test cluster for visualisation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "#plt = df.plot(x='x',y='y',kind='scatter', title='Test Cluster')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "cluster_repr_old = butils.delta_medoids_one_shot(df, 0.2, distance.cosine)\n", - "cluster_repr = butils.delta_medoids_full(df, 0.02, distance.cosine)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cluster_repr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "#cluster_repr.plot(x='x',y='y',kind='scatter', title='cluster_repr')\n", - "#cluster_repr_old.plot(x='x',y='y',kind='scatter', title='cluster_repr_old')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/jupyter/testbed.ipynb b/jupyter/testbed.ipynb index 4d0ad2c..9b8f67f 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -48,7 +48,7 @@ "# overlap.csv\n", "# pendigit.csv\n", "\n", - "df = pd.read_csv(\"data/noisy_circles_3d.csv\", index_col=0)\n", + "df = pd.read_csv(\"data/mfeat-mor.csv\", index_col=0)\n", "df" ] }, @@ -61,6 +61,17 @@ "Visualize data if they have 2 or 3 features." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grouped = df.groupby('cluster')\n", + "for group in grouped:\n", + " print(group)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -76,7 +87,7 @@ " grouped = df.groupby('cluster')\n", " \n", " i = 0\n", - " for group in grouped:\n", + " for key, group in grouped:\n", " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", " label=key, color=colors[i])\n", " i = i + 1\n", @@ -148,7 +159,6 @@ "res_2_len = []\n", "for name in names:\n", " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - "\n", " result = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", " result['cluster'] = name\n", " results2[name] = result\n", @@ -162,12 +172,12 @@ "metadata": {}, "outputs": [], "source": [ - "#using random select algorithm \n", + "#using random select algorithm #TODO use same numbers as the best method found\n", "results3 = {}\n", "res_3_len = []\n", - "for name in names:\n", + "for name, len_num in zip(names, res_len):\n", " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - " result = butils.random_selection(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean, 10)\n", + " result = butils.random_selection(delta_df, butils.estimate_delta(delta_df, distance.euclidean), len_num)\n", " result['cluster'] = name\n", " results3[name] = result\n", " res_3_len.append(result.shape[0])\n", @@ -180,7 +190,16 @@ "metadata": {}, "outputs": [], "source": [ - "#using greedy algorithm" + "#using greedy algorithm\n", + "results4 = {}\n", + "res_4_len = []\n", + "for name in names:\n", + " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", + " result = butils.greedy_select(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", + " result['cluster'] = name\n", + " results4[name] = result\n", + " res_4_len.append(result.shape[0])\n", + "print(res_4_len)" ] }, { @@ -193,12 +212,13 @@ "train_delta_medoids_full = pd.DataFrame()\n", "train_delta_medoids_one_shot = pd.DataFrame()\n", "train_random_selection = pd.DataFrame()\n", + "train_greedy_select = pd.DataFrame()\n", "\n", "for name in names:\n", " train_delta_medoids_full = train_delta_medoids_full.append(results[name])\n", " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(results2[name])\n", " train_random_selection = train_random_selection.append(results3[name])\n", - " " + " train_greedy_select = train_greedy_select.append(results4[name])" ] }, { @@ -268,6 +288,17 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "matrix_greedy_select = classifyPoints(train_greedy_select, full_test_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], "source": [ "def benchmark_result_plots(matrices, titles, headers):\n", " \"\"\"\n", @@ -308,9 +339,11 @@ " text = ax.text(j, i, matrices[index][i, j], ha=\"center\", va=\"center\", color = \"r\",\n", " weight=\"bold\", fontsize=16)\n", " \n", - " ax.set_title(titles[index])\n", + " ax.set_title(titles[index], fontsize=16)\n", " \n", - "benchmark_result_plots([matrix_full, matrix_one_shot, matrix_random_selection], ['Delta-Medoids Full', 'Delta-Medoids One Shot', 'Random Selection'], [names, names, names])" + "benchmark_result_plots([matrix_full, matrix_one_shot, matrix_random_selection, matrix_greedy_select],\n", + " ['Delta-Medoids Full', 'Delta-Medoids One Shot', 'Random Selection', 'Greedy Selection'],\n", + " [names, names, names, names])" ] } ], From 3e1447b6a856b41a9db13efe431873c36e6f0882 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Wed, 6 Mar 2019 17:01:12 +0100 Subject: [PATCH 11/65] added mfeat datasets to testbed --- jupyter/data/mfeat-fac.csv | 2001 ++++++++++++++++++++++++++++++++++++ jupyter/data/mfeat-fou.csv | 2001 ++++++++++++++++++++++++++++++++++++ jupyter/data/mfeat-kar.csv | 2001 ++++++++++++++++++++++++++++++++++++ jupyter/data/mfeat-pix.csv | 2001 ++++++++++++++++++++++++++++++++++++ jupyter/testbed.ipynb | 7 +- 5 files changed, 8009 insertions(+), 2 deletions(-) create mode 100644 jupyter/data/mfeat-fac.csv create mode 100644 jupyter/data/mfeat-fou.csv create mode 100644 jupyter/data/mfeat-kar.csv create mode 100644 jupyter/data/mfeat-pix.csv diff --git a/jupyter/data/mfeat-fac.csv b/jupyter/data/mfeat-fac.csv new file mode 100644 index 0000000..543d949 --- /dev/null +++ b/jupyter/data/mfeat-fac.csv @@ -0,0 +1,2001 @@ +,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,cluster 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a/jupyter/testbed.ipynb b/jupyter/testbed.ipynb index 9b8f67f..5ff0bc6 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -43,13 +43,16 @@ "# iris.csv\n", "# mfeat-mor.csv\n", "# mfeat-zer.csv\n", + "# mfeat-pix.csv\n", + "# mfeat-fac.csv\n", + "# mfeat-fou.csv\n", + "# mfeat-kar.csv\n", "# noisy_circles.csv\n", "# noisy_circles_3d.csv\n", "# overlap.csv\n", "# pendigit.csv\n", "\n", - "df = pd.read_csv(\"data/mfeat-mor.csv\", index_col=0)\n", - "df" + "df = pd.read_csv(\"data/mfeat-kar.csv\", index_col=0)" ] }, { From 777b754e3ef7f973477b820bbf25d346b5e7f19e Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Wed, 6 Mar 2019 17:03:18 +0100 Subject: [PATCH 12/65] added task of thesis --- text/task.txt | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 text/task.txt diff --git a/text/task.txt b/text/task.txt new file mode 100644 index 0000000..7afb2f9 --- /dev/null +++ b/text/task.txt @@ -0,0 +1,15 @@ +The goal of this thesis is to create a memory efficient representation of +network host behavioural clusters used in the Cognitive targeted anomaly +detection framework. The used behavioural similarity measure does not form a +metric space. Therefore the non-metric cluster representation is needed. + +Study the state-of-the-art literature on the topic of cluster representations in +non-metric spaces. + +Create benchmark datasets and use them to compare the memory and computational +requirements of existing methods for cluster representations in non-metric +spaces. + +Analyze the results, select the best method, and incorporate it into the +Cognitive targeted anomaly detection framework and fine-tune its parameters for +the network security domain. From f8ff7d353cf33731e7b316e4a9439457b4961c43 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 10 Mar 2019 17:31:40 +0100 Subject: [PATCH 13/65] Introduction first draft --- text/hlavac-thesis.tex | 129 ++++++++++++++++++++++++++++++++++++----- 1 file changed, 116 insertions(+), 13 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index a0b2dda..7b8c984 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -12,7 +12,7 @@ \documentclass[thesis=B,english]{FITthesis}[2012/10/20] -% \usepackage[utf8]{inputenc} % LaTeX source encoded as UTF-8 +\usepackage[utf8]{inputenc} % LaTeX source encoded as UTF-8 % \usepackage[latin2]{inputenc} % LaTeX source encoded as ISO-8859-2 % \usepackage[cp1250]{inputenc} % LaTeX source encoded as Windows-1250 @@ -23,6 +23,10 @@ \usepackage{dirtree} %directory tree visualisation +\usepackage{csquotes} + +%\usepackage[main=english,czech]{babel} + % % list of acronyms % \usepackage[acronym,nonumberlist,toc,numberedsection=autolabel]{glossaries} % \iflanguage{czech}{\renewcommand*{\acronymname}{Seznam pou{\v z}it{\' y}ch zkratek}}{} @@ -32,19 +36,19 @@ % EDIT THIS % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % -\department{Department of \ldots (SPECIFY)} -\title{Thesis title (SPECIFY)} -\authorGN{Your Given Name(s) (SPECIFY)} %author's given name/names -\authorFN{Your Family Name (surname, SPECIFY)} %author's surname -\author{Your Name (SPECIFY)} %author's name without academic degrees -\authorWithDegrees{Your Name \& degrees} %author's name with academic degrees -\supervisor{Your Supervisor's Name (SPECIFY)} -\acknowledgements{THANKS (remove entirely in case you do not with to thank anyone)} +\department{Department of Computer Systems} +\title{ Memory efficient cluster representations in non-metric spaces} +\authorGN{Jaroslav} %author's given name/names +\authorFN{HlaváÄ} %author's surname +\author{Jaroslav HlaváÄ} %author's name without academic degrees +\authorWithDegrees{Jaroslav HlaváÄ} %author's name with academic degrees +\supervisor{Ing. Martin Kopp} +\acknowledgements{THANKS (remove entirely in case you do not with to thank anyone)} %TODO \abstractEN{Summarize the contents and contribution of your work in a few sentences in English language.} -\abstractCS{V n{\v e}kolika v{\v e}t{\' a}ch shr{\v n}te obsah a p{\v r}{\' i}nos t{\' e}to pr{\' a}ce v {\v c}esk{\' e}m jazyce.} +\abstractCS{V n{\v e}kolika v{\v e}t{\' a}ch shr{\v n}te obsah a p{\v r}{\' i}nos t{\' e}to pr{\' a}ce v {\v c}esk{\' e}m jazyce.} %TODO \placeForDeclarationOfAuthenticity{Prague} -\keywordsCS{Replace with comma-separated list of keywords in Czech.} -\keywordsEN{Replace with comma-separated list of keywords in English.} +\keywordsCS{Replace with comma-separated list of keywords in Czech.} %TODO +\keywordsEN{Replace with comma-separated list of keywords in English.} %TODO \declarationOfAuthenticityOption{1} %select as appropriate, according to the desired license (integer 1-6) % \website{http://site.example/thesis} %optional thesis URL @@ -57,18 +61,117 @@ \setsecnumdepth{part} \chapter{Introduction} +While network communication over the Internet is very efficient and helps everyday life of many people around the world, there is a lot of security issues concerning the shared medium infrastructure of the Internet. +Networks are becoming very complex as new services are being deployed. +Every day security engineers are trying to prevent different kind of threats - ranging from spam flooding user inboxes to bruteforcing specific devices on the edge of internal networks. +Once it might have been enough to set up a firewall and change default usernames and passwords. +Now there is need for much more sophisticated defense mechanisms. +Even though multi-layered defense systems are deployed on networks, there is still is many exploits in software and hardware that attackers can use to breach the defense mechanisms and hack themselves into a network. +Once they are inside a network it often takes a long time to notice that something is amiss. +This problem can tackled by comparing the networks current behavior compared to how it is expected to behave. +Usualy a border device in that given network is gathering information about behavior and if it detects that the network behaves differently than expected security personel are notified about possible anomaly. \\ + +In big networks of 1000 network hosts and more small anomalies can be difficult to find by the method explained above. +For example single infected printer that is used in DDoS botnet can be easily missed in the traffic of a 3000 employee company. +This is the scenario that is seen very often lately and few different methods were designed to find these kinds of anomalies. \\ + +The method I focus on in this work is used in Cognitive targeted anomaly detection framework and is based on clustering the network hosts based on their behavior and each cluster is monitored separately. +In each cluster there are devices that are behaving in similar manner, therefore any anomalous behavior for such device is easier to detect. +The problem that arises with this method is how to represent these clusters. +In each cluster there can be up to several thousand of hosts which is not convenient while trying to detect anomalies in real time. +I modified, implemented and tested 4 algorithms for cluster representation in non-metric space as the similarity measure used does not form a metric space. + +%Why use clustering methods in network anomaly detection? +%Why do we need representative selection? +%What are the next sections about? +% +%Automatically grouping similar objects from a given set into clusters is a +%widely used machine learning technique in many fields ranging from +%recommendation engines and social network analysis to image segmentation and +%medical imaging. New applications of this powerfull method are found every year. +%Clustering is also used for Anomaly Detection in computer systems. \par +%In this work I collected 5TODO known methods used in different fields for +%cluster representation. What these methods have in common is possibility to use +%them in spaces where only generic similarity measure exists (these spaces do +%not necessarily need to be metric). + + \setsecnumdepth{all} \chapter{State-of-the-art} +How is this kind of work done nowadays? Cisco method published and working. +Other methods used - always worse. +Explain the specifics of this method. \chapter{Analysis and design} +Explain what algorithms were implemented and tested. +Explain what are the articles about and why I chose them. +Explanation of datasets I have used. + +%% good to make a small paragraph what is this chapter about good for readability +%V této kapitole provedeme analýzu požadavků a návrh vÄetnÄ› zdůvodnÄ›ní vÅ¡ech +%rozhodnutí. +% +%Also we could write what sections do we have here. +% +%\section[Requirememnts]{Requirement analysis} +% +%Text -- zejména ten odborný -- je nutné Älenit na odstavce. Každý odstavec by se +%mÄ›l týkat jednoho tématu, myÅ¡lenky\dots{} Odstavce od sebe musí být vizuálnÄ› +%oddÄ›lené. K tomu existuje nÄ›kolik vhodných stylů, které si popíšeme v jedné z +%následujících kapitol. Odstavce mohou být různÄ› vysázené. V odborných textech je +%běžná sazba ``do bloku''. PÅ™i ní je nutné vhodnÄ› mÄ›nit mezislovní mezery. Jejich +%doporuÄená velikost je 0,25--0,33~ÄtverÄíku. \enquote{something quoted} \\ +%% ~ -> nezalomitelna mezera +% +% +%\noindent +%Požadavky jsou tÄ›chto typů: +%\begin{description} +% \item[funkÄní] objasňují, co se musí udÄ›lat a identifikují nutné aktivity; +% \item[nefunkÄní] jsou vÅ¡echny, které nejsou funkÄní. Typicky mezi nÄ› patří: +% \begin{itemize} +% \item výkonnostní, +% \item designové. +% \end{itemize} +%\end{description} +% +%\subsection{FunkÄní požadavky} +% +%FunkÄní požadavky této práce jsou: +%\begin{enumerate} +% \item získat zadání, +% \item sepsat práci, +% \item vÄas odevzdat, +% \item obhájit\footnote{Když se zadaří.}. +%\end{enumerate} +% +%\section{Tabulky} +% +%V tabulce \ref{tab:body} najdete možnosti, jak získat body v pÅ™edmÄ›tu BI-DPR. +% +%\begin{table}[th] \centering +%\caption{Bodované Äinnosti pÅ™edmÄ›tu BI-DPR} \label{tab:body} +% \begin{tabular}{|l|r|c|} +% \hline +% Äinnost & body & povinná \\ \hline \hline +% test citace & 10 & ne \\ \hline +% test typografie & 10 & ne \\ \hline +% hodnocení vedoucího & 20 & ne \\ \hline +% ohbahoba poziÄní zprávy & 60 & ano \\ \hline +% \end{tabular} +%\end{table} \chapter{Realisation} +Implementations of given algorithms with explanation and memory efficiency shown. +Why is selecting delta a problem, how do I select it. +Testbed realisation and results. \setsecnumdepth{part} \chapter{Conclusion} - +Algorithms has been tested. +Which one is the best and under what circumstances? \bibliographystyle{iso690} \bibliography{mybibliographyfile} From b26c58a2dd019f2d1dd599066023ee8c9f0f3534 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 10 Mar 2019 18:03:16 +0100 Subject: [PATCH 14/65] drafted chapters with questions --- text/hlavac-thesis.tex | 37 ++++++++++++++++++++++++------------- 1 file changed, 24 insertions(+), 13 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 7b8c984..833c5b6 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -99,15 +99,31 @@ \chapter{Introduction} \setsecnumdepth{all} -\chapter{State-of-the-art} -How is this kind of work done nowadays? Cisco method published and working. -Other methods used - always worse. -Explain the specifics of this method. +\chapter{Network anomaly detection} -\chapter{Analysis and design} -Explain what algorithms were implemented and tested. -Explain what are the articles about and why I chose them. -Explanation of datasets I have used. +What are the challenges in anomaly detection? +What are the possible methods to tackle these problems? +How can machine learning help? What are the challenges of using machine learning? +Introduction to Cognitive targeted anomaly detection. + +\chapter{Representative selection in non-metric spaces} + +Why is it a non-metric space? +What are the algorithms that can be used for representative selection in non-metric spaces? +Problems that need to be tackled using this method. + +\chapter{Comparison of methods} + +Testbed explanation - why is it the best etc. +What datasets am I using and why? +What criteria were chosen to compare algoritms? +Algorithms comparison. +Summary on what algorithm was chosen and why is it the best one. + +\chapter{Testing the best method in reality} + +What were the parameters when this method has been used? +How did it perform compared to random selection? %% good to make a small paragraph what is this chapter about good for readability %V této kapitole provedeme analýzu požadavků a návrh vÄetnÄ› zdůvodnÄ›ní vÅ¡ech @@ -163,11 +179,6 @@ \chapter{Analysis and design} % \end{tabular} %\end{table} -\chapter{Realisation} -Implementations of given algorithms with explanation and memory efficiency shown. -Why is selecting delta a problem, how do I select it. -Testbed realisation and results. - \setsecnumdepth{part} \chapter{Conclusion} Algorithms has been tested. From e3a0509fb94fe9d8317bf26e5255ca8b5ccd3fb2 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 10 Mar 2019 18:42:42 +0100 Subject: [PATCH 15/65] docs added to greedy and random algorithms --- jupyter/bc_utils.py | 40 +++++++++++++++++++++++++++++++++------- jupyter/testbed.ipynb | 6 +++--- 2 files changed, 36 insertions(+), 10 deletions(-) diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index 8b23eae..3a7fd00 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -215,8 +215,16 @@ def estimate_delta(df, similarity_measure): delta = np.sort(similarities)[similarities.size / 10] return delta * 1.05 #TODO store this value somewhere better -#using random select algorithm -def random_selection(df, delta, num): +def random_select(df, num): + """Returns random subset from input DataFrame of given size. + + :param df: in data + :type df: pandas.DataFrame + :param num: number of representatives to select + :type num: int + + :Example: + >>> ret_df = random_select(input_df, 5)""" rows = random.sample(df.index, num) representatives = df.loc[rows] return representatives @@ -225,6 +233,22 @@ def random_selection(df, delta, num): # pandas.DataFrame in_df # float delta def greedy_select(df, delta, similarity_measure): + """Returns subset from input DataFrame selected by greedy algorithm that + that always looks for representative that if the farthest from all + previously selected. + + All others are calculate by finding the smallest sum of similarities to all + representatives previously selected. + + :param df: in data + :type df: pandas.DataFrame + :param delta: maximum distance of points to be considered similar + :type delta: float + :param similarity_measure: similarity function to be used in algorithm + :type similarity_measure: scipy.spacial.distance + + :Example: + >>> ret_df = greedy_select(input_df, 0.25, distance.cosine)""" medoid_indexes = np.array([]) index_list = np.array(df.index.values) @@ -238,13 +262,13 @@ def greedy_select(df, delta, similarity_measure): else: for index in index_list: current_index = None - diff_sum = 0 - tmp_sum = 0 + similarity_sum = 0 # used to store the biggest sum + tmp_sum = 0 # used to store the current sum for medoid in medoid_indexes: tmp_sum = tmp_sum + similarity_measure(df.loc[index].values[:], df.loc[medoid].values[:]) - if tmp_sum > diff_sum: - diff_sum = tmp_sum + if tmp_sum > similarity_sum: + similarity_sum = tmp_sum current_index = index rem_indexes = np.array([]) @@ -254,11 +278,13 @@ def greedy_select(df, delta, similarity_measure): if distance.euclidean(df.loc[ix].values[:], df.loc[current_index].values[:]) <= delta: rem_indexes = np.append(rem_indexes, ix) + # adding selected index to other representatives medoid_indexes = np.append(medoid_indexes, current_index) + # dropping points from the delta distance of newly selected representative index_list = np.array([item for item in index_list if item not in rem_indexes]) rem_indexes = np.delete(rem_indexes, np.where(rem_indexes == current_index)) - df = df.drop(rem_indexes) + return df diff --git a/jupyter/testbed.ipynb b/jupyter/testbed.ipynb index 5ff0bc6..c8c166f 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -52,7 +52,7 @@ "# overlap.csv\n", "# pendigit.csv\n", "\n", - "df = pd.read_csv(\"data/mfeat-kar.csv\", index_col=0)" + "df = pd.read_csv(\"data/pendigit.csv\", index_col=0)" ] }, { @@ -180,7 +180,7 @@ "res_3_len = []\n", "for name, len_num in zip(names, res_len):\n", " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - " result = butils.random_selection(delta_df, butils.estimate_delta(delta_df, distance.euclidean), len_num)\n", + " result = butils.random_select(delta_df, len_num)\n", " result['cluster'] = name\n", " results3[name] = result\n", " res_3_len.append(result.shape[0])\n", @@ -245,7 +245,7 @@ " X_test = scaler.transform(X_test)\n", "\n", " from sklearn.neighbors import KNeighborsClassifier \n", - " classifier = KNeighborsClassifier(n_neighbors=5)\n", + " classifier = KNeighborsClassifier(n_neighbors=1)\n", " classifier.fit(X_train, y_train)\n", "\n", " y_pred = classifier.predict(X_test)\n", From a7742ace5f35b95db7c92c58311a4e0af08d3cb7 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 15 Mar 2019 09:49:03 +0100 Subject: [PATCH 16/65] removed obsoleted files, added sections --- jupyter/blobs2d.ipynb | 266 ---------------------- jupyter/circles2d.ipynb | 259 --------------------- jupyter/iris.ipynb | 183 --------------- jupyter/make_datasets.ipynb | 11 + jupyter/moons2d.ipynb | 440 ++++++++++++++++++++++++++++++++++-- text/hlavac-thesis.tex | 3 + 6 files changed, 441 insertions(+), 721 deletions(-) delete mode 100644 jupyter/blobs2d.ipynb delete mode 100644 jupyter/circles2d.ipynb delete mode 100644 jupyter/iris.ipynb diff --git a/jupyter/blobs2d.ipynb b/jupyter/blobs2d.ipynb deleted file mode 100644 index bda4422..0000000 --- a/jupyter/blobs2d.ipynb +++ /dev/null @@ -1,266 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Blobs in 2D Datasets Notebook\n", - "\n", - "Testing my algorithms on 2D generated datasets with centers approaching together." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets.samples_generator import make_blobs\n", - "from sklearn.model_selection import train_test_split\n", - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib import pyplot\n", - "from scipy.spatial import distance\n", - "\n", - "from bc_utils import TestDf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating datasets and saving them to pandas dataframes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cluster_deviations = np.array(range(1, 7), dtype=np.float32) / 10\n", - "dataset_dfs = []\n", - "for std_deviation in cluster_deviations:\n", - " X, y = make_blobs(n_samples=250, centers=2, cluster_std=std_deviation,\n", - " n_features=2) #center 1 priblizovat...\n", - " new_dataframe = pd.DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", - " dataset_dfs.append(new_dataframe)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Showing plots of the datasets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "colors = {0:'red', 1:'blue'}\n", - "for df in dataset_dfs:\n", - " fix, ax = pyplot.subplots()\n", - " grouped = df.groupby('label')\n", - " for key, group in grouped:\n", - " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", - " label=key, color=colors[key])\n", - " pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Separating datasets to learn and test parts. Using own class TestDataset from utils.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = dataset_dfs[0] \n", - "\n", - "#creating dataset for testing algorithms\n", - "learning_dataset = TestDf(dataset)\n", - "\n", - "learning_dataset.show_plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "learning_datasets = []\n", - "for dataset in dataset_dfs:\n", - " learning_datasets.append(TestDf(dataset))\n", - " \n", - "for ldataset in learning_datasets:\n", - " ldataset.show_plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# shows histograms based on distance from first point in dataset\n", - "def training_histogram(in_df):\n", - " base_point = in_df.values[0]\n", - "\n", - " vals = in_df.values\n", - " histogram_data = []\n", - " for item in vals:\n", - " histogram_data.append(distance.euclidean(\n", - " [base_point[1], base_point[2]],\n", - " [item[1], item[2]]))\n", - "\n", - " #for i in range(10, 60, 10):\n", - " bins = len(histogram_data) / 3\n", - " pyplot.hist(histogram_data, bins, facecolor='blue', alpha=0.5)\n", - " pyplot.show()\n", - " \n", - " mean_distance = np.mean(histogram_data)\n", - " print(\"Mean distance for this dataset: \" + str(mean_distance) + \"\\n\")\n", - " return mean_distance\n", - "\n", - "# finding representation of a cluster with random selection\n", - "# pandas.DataFrame in_df\n", - "# float delta\n", - "def randomSelection(in_df, delta):\n", - " \n", - " medoid_indexes = []\n", - " index_list = list(in_df.index.values)\n", - " \n", - " while (index_list):\n", - " #print(\"Index length: \" + str(len(index_list)))\n", - " # select first unvisited index\n", - " ind = in_df.loc[index_list[0]].name\n", - " #print(\"Selected index for this run: \" + str(ind))\n", - " ref_point = [in_df.loc[ind].values[1], in_df.loc[ind].values[2]]\n", - " rem_indexes = []\n", - " #print(\"Lenght of in_df: \" + str(len(in_df)))\n", - " \n", - " # find indexes to drop from dataframe\n", - " for index, row in in_df.iterrows():\n", - " if distance.euclidean([row['x'], row['y']], ref_point) <= delta:\n", - " rem_indexes.append(index)\n", - " \n", - " index_list = [item for item in index_list if item not in rem_indexes]\n", - " #print(\"Droping these indexes: \" + str(rem_indexes))\n", - " rem_indexes.remove(ind)\n", - " medoid_indexes.append(ind)\n", - " \n", - " in_df = in_df.drop(rem_indexes)\n", - " #print(\"These indexes remain in in_df: \" + str(in_df.index.values))\n", - " \n", - " return in_df\n", - " \n", - "\n", - " \n", - "medoid_lst = [] \n", - "for df in learning_datasets: #[0:1]:\n", - " \n", - " print(\"Datagram for 0\")\n", - " mean_0 = training_histogram(df.train_df.loc[df.train_df[\"label\"] == 0])\n", - " medoid_0 = randomSelection(df.train_df.loc[df.train_df[\"label\"] == 0], mean_0)\n", - " \n", - " print(\"Datagram for 1\")\n", - " mean_1 = training_histogram(df.train_df.loc[df.train_df[\"label\"] == 1])\n", - " medoid_1 = randomSelection(df.train_df.loc[df.train_df[\"label\"] == 1], mean_1)\n", - "\n", - " medoid_lst.append([df, pd.concat([medoid_0, medoid_1])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Moving to classify points if they fit to the right dataset by KNeighbors algorithm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def classifyPoints(test):\n", - "\n", - " X_train = test[0].test_df.iloc[:, 1:].values\n", - " y_train = test[0].test_df.iloc[:, 0].values\n", - " X_test = test[1].iloc[:, 1:].values\n", - " y_test = test[1].iloc[:, 0].values\n", - "\n", - " from sklearn.preprocessing import StandardScaler \n", - " scaler = StandardScaler() \n", - " scaler.fit(X_train)\n", - "\n", - " X_train = scaler.transform(X_train) \n", - " X_test = scaler.transform(X_test)\n", - "\n", - " from sklearn.neighbors import KNeighborsClassifier \n", - " classifier = KNeighborsClassifier(n_neighbors=3)\n", - " classifier.fit(X_train, y_train)\n", - "\n", - " y_pred = classifier.predict(X_test)\n", - "\n", - " from sklearn.metrics import classification_report, confusion_matrix \n", - " print(confusion_matrix(y_test, y_pred)) \n", - " print(classification_report(y_test, y_pred))\n", - " \n", - "for dataset in medoid_lst:\n", - " classifyPoints(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#print(learning_datasets[0].train_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/jupyter/circles2d.ipynb b/jupyter/circles2d.ipynb deleted file mode 100644 index 41737b3..0000000 --- a/jupyter/circles2d.ipynb +++ /dev/null @@ -1,259 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Blobs in 2D Datasets Notebook\n", - "\n", - "Testing my algorithms on 2D generated datasets with centers approaching together." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets.samples_generator import make_circles\n", - "from sklearn.model_selection import train_test_split\n", - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib import pyplot\n", - "from scipy.spatial import distance\n", - "\n", - "from bc_utils import TestDf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creating datasets and saving them to pandas dataframes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "noise_vals = [0.01, 0.05, 0.1, 0.15]\n", - "dataset_dfs = []\n", - "for noise in noise_vals:\n", - " X, y = make_circles(n_samples=250, noise=0.2)\n", - " new_dataframe = pd.DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", - " new_dataframe = new_dataframe\n", - " dataset_dfs.append(new_dataframe)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Showing plots of the datasets." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "colors = {0:'red', 1:'blue'}\n", - "for df in dataset_dfs:\n", - " fix, ax = pyplot.subplots()\n", - " grouped = df.groupby('label')\n", - " for key, group in grouped:\n", - " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", - " label=key, color=colors[key])\n", - " pyplot.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Separating datasets to learn and test parts. Using own class TestDataset from utils.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = dataset_dfs[0] \n", - "\n", - "#creating dataset for testing algorithms\n", - "learning_dataset = TestDf(dataset)\n", - "\n", - "learning_dataset.show_plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "learning_datasets = []\n", - "for dataset in dataset_dfs:\n", - " learning_datasets.append(TestDf(dataset))\n", - " \n", - "for ldataset in learning_datasets:\n", - " ldataset.show_plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# shows histograms based on distance from first point in dataset\n", - "def training_histogram(in_df):\n", - " base_point = in_df.values[0]\n", - "\n", - " vals = in_df.values\n", - " histogram_data = []\n", - " for item in vals:\n", - " histogram_data.append(distance.euclidean(\n", - " [base_point[1], base_point[2]],\n", - " [item[1], item[2]]))\n", - "\n", - " #for i in range(10, 60, 10):\n", - " bins = len(histogram_data) / 3\n", - " pyplot.hist(histogram_data, bins, facecolor='blue', alpha=0.5)\n", - " pyplot.show()\n", - " \n", - " mean_distance = np.mean(histogram_data)\n", - " print(\"Mean distance for this dataset: \" + str(mean_distance) + \"\\n\")\n", - " return mean_distance\n", - "\n", - "# finding representation of a cluster with random selection\n", - "# pandas.DataFrame in_df\n", - "# float delta\n", - "def randomSelection(in_df, delta):\n", - " \n", - " medoid_indexes = []\n", - " index_list = list(in_df.index.values)\n", - " \n", - " while (index_list):\n", - " #print(\"Index length: \" + str(len(index_list)))\n", - " # select first unvisited index\n", - " ind = in_df.loc[index_list[0]].name\n", - " #print(\"Selected index for this run: \" + str(ind))\n", - " ref_point = [in_df.loc[ind].values[1], in_df.loc[ind].values[2]]\n", - " rem_indexes = []\n", - " #print(\"Lenght of in_df: \" + str(len(in_df)))\n", - " \n", - " # find indexes to drop from dataframe\n", - " for index, row in in_df.iterrows():\n", - " if distance.euclidean([row['x'], row['y']], ref_point) <= delta:\n", - " rem_indexes.append(index)\n", - " \n", - " index_list = [item for item in index_list if item not in rem_indexes]\n", - " #print(\"Droping these indexes: \" + str(rem_indexes))\n", - " rem_indexes.remove(ind)\n", - " medoid_indexes.append(ind)\n", - " \n", - " in_df = in_df.drop(rem_indexes)\n", - " #print(\"These indexes remain in in_df: \" + str(in_df.index.values))\n", - " \n", - " return in_df\n", - " \n", - "\n", - " \n", - "medoid_lst = [] \n", - "for df in learning_datasets: #[0:1]:\n", - " \n", - " print(\"Datagram for 0\")\n", - " mean_0 = training_histogram(df.train_df.loc[df.train_df[\"label\"] == 0])\n", - " medoid_0 = randomSelection(df.train_df.loc[df.train_df[\"label\"] == 0], 0.05)\n", - " \n", - " print(\"Datagram for 1\")\n", - " mean_1 = training_histogram(df.train_df.loc[df.train_df[\"label\"] == 1])\n", - " medoid_1 = randomSelection(df.train_df.loc[df.train_df[\"label\"] == 1], 0.05)\n", - "\n", - " medoid_lst.append([df, pd.concat([medoid_0, medoid_1])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Moving to classify points if they fit to the right dataset by KNeighbors algorithm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def classifyPoints(test):\n", - "\n", - " X_train = test[0].test_df.iloc[:, 1:].values\n", - " y_train = test[0].test_df.iloc[:, 0].values\n", - " X_test = test[1].iloc[:, 1:].values\n", - " y_test = test[1].iloc[:, 0].values\n", - "\n", - " from sklearn.preprocessing import StandardScaler \n", - " scaler = StandardScaler() \n", - " scaler.fit(X_train)\n", - "\n", - " X_train = scaler.transform(X_train) \n", - " X_test = scaler.transform(X_test)\n", - "\n", - " from sklearn.neighbors import KNeighborsClassifier \n", - " classifier = KNeighborsClassifier(n_neighbors=3)\n", - " classifier.fit(X_train, y_train)\n", - "\n", - " y_pred = classifier.predict(X_test)\n", - "\n", - " from sklearn.metrics import classification_report, confusion_matrix \n", - " print(confusion_matrix(y_test, y_pred)) \n", - " print(classification_report(y_test, y_pred))\n", - " \n", - "for dataset in medoid_lst:\n", - " classifyPoints(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/jupyter/iris.ipynb b/jupyter/iris.ipynb deleted file mode 100644 index c4deefb..0000000 --- a/jupyter/iris.ipynb +++ /dev/null @@ -1,183 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "import matplotlib.pyplot as plt \n", - "import pandas as pd\n", - "\n", - "import bc_utils as butils\n", - "\n", - "from scipy.spatial import distance\n", - "\n", - "from sklearn.model_selection import train_test_split \n", - "from sklearn.preprocessing import StandardScaler \n", - "from sklearn.neighbors import KNeighborsClassifier " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Read dataset to pandas dataframe \n", - "df = pd.read_csv(\"iris.csv\")\n", - "df " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "#creating test datasets for iris\n", - "names = ['setosa', 'versicolor', 'virginica']\n", - "\n", - "dataframes = {}\n", - "for name in names:\n", - " tmp_df = df[df['species'] == name]\n", - " dataframes[name] = butils.TestDf(tmp_df)\n", - "\n", - "full_test_df = pd.DataFrame()\n", - "for name in names:\n", - " full_test_df = full_test_df.append(dataframes[name].test_df)\n", - "\n", - "#changing last column name to 'cluster' #TODO do it better\n", - "cols = list(full_test_df.columns[:-1])\n", - "cols.append('cluster')\n", - "full_test_df.columns = cols" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#using delta_medoids_full algorithm\n", - "results = {}\n", - "for name in names:\n", - " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - "\n", - " result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.cosine), distance.cosine)\n", - " result['cluster'] = name\n", - " results[name] = result\n", - " \n", - "print(results)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#using delta_medoids_one_shot algorithm\n", - "results2 = {}\n", - "for name in names:\n", - " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - "\n", - " result = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.cosine), distance.cosine)\n", - " result['cluster'] = name\n", - " results2[name] = result\n", - " \n", - "print(results2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#creating training DataFrames for comparing oneshot and full delta medoids algorithm\n", - "train_delta_medoids_full = pd.DataFrame()\n", - "train_delta_medoids_one_shot = pd.DataFrame()\n", - "\n", - "for name in names:\n", - " train_delta_medoids_full = train_delta_medoids_full.append(results[name])\n", - " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(results2[name])\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def classifyPoints(ref_df, test_df):\n", - "\n", - " X_train = ref_df.iloc[:, :-1].values\n", - " y_train = ref_df.iloc[:, -1].values\n", - " X_test = test_df.iloc[:, :-1].values\n", - " y_test = test_df.iloc[:, -1].values\n", - "\n", - " from sklearn.preprocessing import StandardScaler \n", - " scaler = StandardScaler() \n", - " scaler.fit(X_train)\n", - "\n", - " X_train = scaler.transform(X_train) \n", - " X_test = scaler.transform(X_test)\n", - "\n", - " from sklearn.neighbors import KNeighborsClassifier \n", - " classifier = KNeighborsClassifier(n_neighbors=5)\n", - " classifier.fit(X_train, y_train)\n", - "\n", - " y_pred = classifier.predict(X_test)\n", - "\n", - " from sklearn.metrics import classification_report, confusion_matrix \n", - " print(confusion_matrix(y_test, y_pred)) \n", - " print(classification_report(y_test, y_pred))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "classifyPoints(train_delta_medoids_full, full_test_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "classifyPoints(train_delta_medoids_one_shot, full_test_df)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/jupyter/make_datasets.ipynb b/jupyter/make_datasets.ipynb index dffc43f..d7d5c0b 100644 --- a/jupyter/make_datasets.ipynb +++ b/jupyter/make_datasets.ipynb @@ -203,6 +203,17 @@ "#df.to_csv('blobs_3d.csv', sep=',', encoding='utf-8')" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"data/mfeat-kar.csv\")\n", + "df.to_csv('data/mfeat-kar.csv', sep=',', encoding='utf-8')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/jupyter/moons2d.ipynb b/jupyter/moons2d.ipynb index c64a05d..80f2584 100644 --- a/jupyter/moons2d.ipynb +++ b/jupyter/moons2d.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -57,11 +57,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "dataset = dataset_dfs[0] \n", "\n", @@ -96,11 +170,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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Nrzq/I+qeiMgyrPmKmTNnJj2m6qTaQkVLlTNPMtS3lJbjJSwB1mPvpAFgV7d13vfHXClVS6onThKV0ZcvX86WLVvYvHkzDz/8MHfffTfr16/HGMOSJUt45JFH6O3t5ZhjjuEXv/gFAHv27GH8+PF87WtfY/Xq1UyZ4pVVmh1Z+zSC6AFmuF5PB14sPsgYs8IYM98YM79V0//9qbZQ0dZWWLBg5B3rV4W2kqqz7s/084H4FTJscG2f3GZzQdzE1f0xqB+6UhFJd3t299OYN28ezzzzDF1dXcyZM4dVq1bxmc98ht/97neMHz8+ng9MiLxrGvcDN4rID4GFwB5jjJqmyiWJAmxJ9IQIG07qNXkPYOtCxZGR7qfltABnYk1SxZwJPAZMaoVrVsL3263zPoZKqcOIozSL4knS3Z7j7qeRFZkKDRHpwBaXniIiPcDfY9euGGNuAx4ALsIGF74BfDSbkVYxcRtp3cQ1ge3HOpQfx2otQT4KR9NZw3Dh8Wov3NENy9pgZoWTtJ8P5KTC46NYDcO4xnocVuhdvhS+rN0fq40EKqOP6Kfx+c9/ng9/+MOMHTuWF154gaamJg4dOsSkSZO48sorGTt2LHfcccew9+bNPJWp0DDGlJy9jK3bfkNKw0mfpBO1Em5fGQuOQ9vxEzhCIMhHMRs4AvgVtrfF+g6byzFqNHz1INweo3CE4d/VSQwJCPd35xaiLVoptRqJu9tz0v00Pv3pT/ODH/yAN954g+nTp/Pxj3+cL37xi5UNOgDtp5EVaZTE3rDBhn3u2TO07aijbOjpggXDj01KgJU6735smGqxYxnC9chw3v9qL3x21nDnc0uLDa+NY+LW8uVVS6R+GnWA9tOoNtIqiR3WSJvUpLgVa+cvNuM4+EUjQbhoLMdMdUe31TDijliCyr4rLfmh1CAqNLIgrZLYYYy0SQmwrcDvC88dk9MaYDIwsfC6VDRS2Gis2VgfxlcT8mBG/a62AmuJLogrETgqrGoO7adR76SZqBVkpE1CgO3HTpjFDAJ3A+/BTqBeuQdzgRPL/OyZrdaHEacH0yHKd+UWmFEEcSWan5rSahLtp1HvpJ2oVSobOgkBVsrsZBg+gcYVuhu3B9Oh3O9qP9YkV0xYQVypOUw7AY7AGIOIV55wfVKpH1uFRlYkkecQhSQEmJ/ZyUEYPoHGFbrrJRzjyFEp57tyBGZUQVyJ5lep1phEPk/GNDc3s2vXLiZPnqyCAyswdu3aRXNzc+RzqNDIkrwkapUrwNyTy9hW79BTRxD5aRtp1EyKM0cl7Hc1Do9CN9jkv7DvjypwwrzXz9+RZD5PhkyfPp2enh560yhxXyU0Nzczffr0yO/XkFulPNyTy4GD8JFC61Qv+/l+4GlsMT+HBmw6Z9J29t5eW2F2f0JhuKXYhhWYgr0uZzGUFFjO+6P4JUq918/fkeW1UnKDhtwq0SgVeeOVLHhHO5xwHoxrHWk/bwHmYR3bTl2oKR7nTYJS1WqTnggrNT1W8n6/95byd2R5rZSqQ4WGMkRQ5I3X5NLYZCu7jmv1t5+3MLzsZBokXUgoCOcavF70upz3V9LDvPi9pfwdWV8rparIe5VbJS3CdIbzmlwG+m1lV8hXf4coHfviJG+tWUv5O6Jcq7ANsJSaQzUNxRIm8qY4WfBgP1y5Eia2DmV858Gx75BUGG4QeQt9dUyOTiVeryi5cq6V5oLUNSo0FEvYqB1ncnmkG55vg/Gt9rgzyefEEbZjX5yklfEfhuIJ/kysX8nLV1J8rbz8W3kTiErqqNBQLOXka4xthVdb4UisKQvsCvY4n+PrjTgSJuMoBeI1wT+GbeAUdE4/bSJPAlHJBBUa9Y57cpoNjO6Fp7vhxDb/nhQ5mDgyz0MrntSLX1eSMBmX+ccvM/9lSgcmlNImtFd53aNCo54pnpxe64DPhUjwijhx9PXBvn0wZgxUkJCafR5a8XU7AXiWkZN8lLBZrwl7DbZ3SLnhylE7HJZaFByN9iqvczS5r14p7mXxepk9KcpMQOvuhvXroaEBBgdh4UKbT1YumeehleoB4tBIOBOQFzuxEVf9RdtHYYMNzqT8RME1jBQepcbo9T8WH6+VdBMhroVVFDS5TylN8WpyV3d5PSnKWEn39VmBMTBg/wDWrYOpU8u/MdLIQyt545YqxuhQianOr27XocKj0588rOAo7nAYZoxhzGt5KYFTQ8S1sEoaFRrVRlwO0gMMn/gmt8EhjwSvsWNtB0Av50HIiWPfPnsjDLg+r6HBbi9XaCSdhxZ44wYVY4TKbPzuCRu8hdOjlBd0MIWR9bCCxpiXgpp1QpwLq6TR5L5qIo6EMeccqwqvBdtadUIr/HNRgld7O5x2mm0ZO2uWdSZEYMwYOwG7GRy028slyZw9943b328f162z2w/jTOqN2OvWiF31u19XauOfjTUFnY/3HepoCaVwJ995jTnMGFuwPgy3SUoT+hLBWVi5cRZWeUM1jWohjvh4r3M0AudRcLIuhQ8XErzGjrUCw11n6mPt8I7z/KOqfGhutiv2deuGr+CjrqCSytkLrRF5rcJPI95VuVN65SyGTFIOTpVgvxAyv+irSjQHTehLlDgXVkmjQqNaiCPM1e8cR7jO4SR4bdgw0nkgTbCiG65pLXvCmDXLqtpxOfmSyNkr68YtNs0lZeN3fBePMrzX+r0+IWRBi4soY9SEvsSJe2GVJCo0qoVK4+O9/BilzuHlPDjUDxPbIk8Yzc35vAkccnvjnoT1YThawl6PasPt7Vb9Mq3x59DkIC+nHoh7YZUUKjTyQBjndiUJY27TAlg/xqiAczjOg4+1Ww3jUD9ctbJ0NdsaILc3rltL2NLtH0J2Smv8yXea0JcaeV9YgQqN7CnHVhzFLh3oxyjx3qVLrQ9jRbfVMMYV7EE1PmH43rilhHuaeQulQsiSaN+bxDmVqkWFRpZEsRU7K04nkiVsz+pSfoxSzGy1PowcTRiZlBApJdzTdhIXVxvu7x8eQpZEuGzYEjNKzaNCI0ui2orLmaTKNS14rZhzFLOfSQmRUsKdEvsqvU6ltJegELK4HfOZ125R8oIKjSyJYisuVzspx7RQShjlIAPYq9us4/+NonGELtlQSrhTYl8l1yvMwiCtsu9xX3ilqlGhkSVRbMVRtJMwmkIVhFXGWUKkrJINQcI9bidxXN9FXH4Wvwv/TLeN1vKr9KvUJCo0sqZc00/USJYgTaEKwirjKiFSdsmGIOEexUlcaoKN47uI08/ideEP9sPGNluVoFSlX6XmyLSMiIhcKCLPisg2EbnFY/81ItIrIpsLfx/PYpyJU1yuIejYKCUhgqiCsMq4SohEKtnglPa4uPA4O+Q+L4LKwcSRkxPU770cvC78R1bCka1D538qxs9Tck1mmoaINALfBBYBPcAGEbnfGPNU0aE/MsbcmPoA80wSjukqCassp4SIn8/CK/N7YCBEyYZS2prHPs/PD2N6qvS7iFNrdELVzjvP1p/v7oYxbbCudWT5djc501KV+MjSPHU6sM0Y80cAEfkhcCl2zaIEkYRjOkdRUqUI4/8t9lnMnQuTJg1N4AsXwmOPgbudzI4d8ZWi9vWZhJ3QK/ku4tIa/SKm9mPbxpYiZ1qqEh9ZmqeOBZ53ve5hKIjRzQdF5A8icreIlGpSWT/09traUL298Z+7HFNZTvGqVtvZCb/5Ddx/v10wT5063EQ1OOhR0TbGzz987nIm9KjfRRwmTHfE1J499rG93W5Po9Kvkluy1DTEY1tx1f+fAx3GmAMich1wJ/CXI04ksgxYBjBz5sy4x5kvNF4+EK9qtQCHCk2I1q2Ds8+Or8dHmM8/fO7JpGMGLKWphIlyCgpVS6PSr5JLshQaPQxvbz8deNF9gDFml+vld4CveJ3IGLMCWAG23Wu8w8wRlcbL10lIpJfPwo2jYSRVijqwWm5aZsBiE+Z+rPH3caw2UCrKKUyoWlqVfpVckaV5agNwvIgcJyKjgcuB+90HiMg018slwNMpji9/OKs/N87qL4g4GjhVCY7PorERRnksiwYHYeLEoWOamuxjXBVt3Z/ve+60zIBOuZmt2O99I1ZYBEU5JdntCpI1sSqJkpmmYYw5JCI3Ag9h1z23G2O2isiXgE5jzP3ATSKyBNvdeDdwTVbjzQVRExWqIHEvbtzVal95BTZtGlnuPMmKtrmoluvkagjD+4O7KRXllFS3KzWxVjViTG1Zc+bPn286OzuzHkZyODecu1Bd0A33PPAwwyN2mrB5BUcnNtJcEbpkSK2wH6tZePUYd9OIzS1Ja/HQ22slqttX0tJioxNyXpKk0t9Qkr/BOM4tIhuNMfODjtOM8Gqj3NWfs9oM23ypRqmGPgWx4hXaW0wDwU74uEsKx1kLJkXKKjuTwPuzOrcXmWaEKxFpbYUFC4JvMrdZyk2YyUKpbrxCe2EoJHY+8GFKZ693dNjZ57xFMHMW3NlR+bjiqgWTIl4h1GvX2kjkvj7Ytat0qHbJEGwfwrp8opy7UgKFhojcKCITkxuCkhjOatPNKOACtC5QreOVS/FOhkqdzMO/cOVO4M+uSL3X9kDffri2HdZV6LhO2sGeAF5lZwYH4Ze/hHvvhdWrh/J/wr6/VNkaR1YvWmQfO0rI6kglcSokjHnqTdgSH5uA24GHTK05QmoVr9WmwXbsUyJRVb6RckN73UUOn+uGxtEMC61qbIKfd8PbWivTUpNysCeEXwi3Mwv2F8qp+BW9DAzBdlFuVH05546LQE3DGPN3wPHASmz0UpeI/B8ReUtyw1JiIanihnVKd7ddUTory23bgk0TmRM2tPcVbESdU3RwYhscKDIjDfRDa9tQH5FKCGtizQFOCHXxir4YvxX+66/DgQOwZQu88Ubp8O5yo+pDhXfHTChHuDHGiMhLwEvY4L2JwN0i8itjzKeTG55SMVVST6okEZMS49QKvMqpb9hg80CMSd75mCiOhuFesY5rhY+uhO+1Ww1joB+uWgljW+sqgMJh1iyYMAEefNA/cdRrhd/RAVdfPaSNNDXBd7/r/1uJ4vJJO7w7MORWRG4CrgZeBr4L3GuM6ReRBqDLGJMrjaPmQ27TJJNm3EVE7AsRd0TJrl1Ww3Bu/mIaG2HJkiowWRVTKjS3ETihFx7sthrG2Nby+2TUWBWCP/3JmqGcMjHG2IWD12+stxdmzhypiQZFGEeJqo+DOENupwAfMMYMc/MYYwZF5JKoA1RyTh4SsCImJZbdZCkEYUqTxFG3KnX8QnOdCLvZrXBaa7SJP85GUDlh6lRbtwxsVQHwX+F3d9vFRDENDaUjjPPu8gkUGsaYL5TYV99lPfJC3Ku5vPSEjtgXomTBwIiTumM7XrcORIaKHzok7XxMDK9giUbgA1gjNAzVlHIiq8L8zmqwCoGf9ur3m2prG1k0E+x7gyKM02r/HgVN7kuCNFXyJFZzeUnAitgXIqmIErftePduePzxkaVJqg4nWOI/e2F3N0xqg0tahwSGQ7m/sypoH1wOUbTX1la4/fbhPo3Ro6NFGOcpak+FRtykqZIntZrLSwJWxA52bq0g7kndySyfPBlmzMjPjVwWxYuaDR3wv9th1Gg4dBCmroTZS4cfX+p35uX7qoL2weUQVXt1TE2PP25fz51bvsBIO+M7CBUacZKkSu6lvSS1mnMSsIq9cVnoyxGjv9KIKKnK0iTFi5pTXKZIPEyR+4E/M7L7jfM7u9fH91Ul7YPDUon22toK558f7XOT8M9VigqNOElqEvfTXpJczeXJGxexT0OYST0PAWKp4bWo+Xk3NPmYIl9ttZO+MNJRPgj0Bfi+aiHcu0CS2mspkvDPVYoKjThJYhIP0l6SXM3l2RsXA3kIEEsVr0VNa5u3KXJqm21aUByRP6qw7d3Aju5g31cNNWbKotx9FhnfQWjBwjhJIgPbq36Uo72AXc1dwVBNoSoPaUwap8Dc88/7t8CuWbwWNWNb4TaPWlDSOlJgACxg6HeWF99Xijj+rLRW+VlkfAehmkbc+KnkQRFVfvvDaC81tJpLErdD8b//e2RXvyqo0F0Zfprp7KVwUZEp8nmfc0xg6LeWJ99XBPIUkVSKXDT0cqFCIwmKJ/GgiKpS+2vMoZgVxQ7FyZNSMLKGAAAaX0lEQVRtPSA3Nb5ItvgtaopNkVMY+r05NDCy2GWefF9lkLeIpCDyFHShQiNpgnwSYSKuasihmBXFDsWjjoLrr4cVK6pykVwZYTTTFuAchhzhjh/D631V5vvKY0RSNaFCI2mCIqrCRlwF3eh1FQZUPl4OxbPOgk98Al56SS+bJzEvVvJiDspjRFI1oUIjaYJ8EnFEXNVdGFD5+IVMzphh/xQfYvKX5ckclMeIpGoisMpttZHLKrfb8HA+lrG/GLfTfG+vvfvcYY9BZTTrmCir3byskKuVvj7bf8S9ss+6KrC7Wm3WQiwvxFnlVqmUIDW/HDNAsdN8Ync+6kRVCeU6FPO0Qq5W8mgOyltEUjWhQiMtgtT8MGYAL6f5n9vqLlY+LdRhGg95NQflKSKpmtDkvmrCK9FvfCv8q0dyVhZahlM6e3/QgdWBs0J249fSU/EnjwlqUXASQ3Pd3jcFVNOoJvyc5lcthQ9kHCtfgw138rpCrkaq3RxUykzp9nlB9f6PYVGhUU2USvRrCYiVT7LHRw023IHsitTVKmmag5LuD++YKV96aUiYHDpkG3Q1Nta2/0uFRrURJXZ+Xa+tZhq1z3MQOW64U+nkkcYKWaOz4iVK8EKpNCcvR76IjcB64onhwsSYIe20Vv1fKjSqkXJi5+/sgGvboXE0DByEq1YCS+PVAnLacCeuyKckV8ganRUvUYIXgtKcvMyUhw4NCQw/so4QSwp1hNcyvb1wXTv074e+Pfbxrnab2/F68NtDk0R13wpxTx79/fZx3bp8OTGrYYy+5DToodzghd7e4GrHbke+u8hlKYEBtev/Uk2jlnF6ffe57uzGJujthnExO8sTro9VbpWUtHMDopiY8pi/EIocBz2EDV5wvq9nnx2Z5gTw2GM2+dDBMVO++CJs3Gg1DTeOH6PYp5Hr7zEiKjRqmbY26C/K4Rjoh/e2JaMFJFSiPUqVlDQjn6KamKoyOivnQQ+OVrB2rZ3AjRk5ebu/r1dfHVnteGAAdu60gsX9vuZmOOYY+95izj4bJk60z2vdP5WpeUpELhSRZ0Vkm4jc4rH/CBH5UWH/OhFpS3+UVYzT78DJ4Whuge+shIXVkykexnzgRVq5AX191qQUxcRUlfkLQU3BcoAxVmDA0KNDsUlwzBi49lq7IGlpsY/LlsGECf4mreJzNjRYgeH4vtJs0pQFmWkaItIIfBNYBPQAG0TkfmPMU67D2oFXjDGzReRy4CvA/0h/tFVMlfY7cHAsbFGqpKQR+bRt20htoRwTU9XlL+Q06MHBLRQc3I5wL5Pg2WfDnDmwY4f9TR11lL/Gt2/fkPnJobGxCkyKMZKleep0YJsx5o8AIvJD4FLALTQuBb5YeH438A0REVNrVRaTpsr6HbiptKNokpFPfX3w1FMjtw8MlGdiqqpyFjlvChbkJ/IyCRoD55wDjz8enI9TlSbFmMnSPHUsw5tK9hS2eR5jjDkE7AEmF59IRJaJSKeIdPbWdJPn+qPYwlZJlZS4y0B4ReoAnHxyFQmBKOS4L33QpO42CTp/CxfC8cdbx/ecOdZvceSR3uevSpNizGSpaYjHtmINIswxGGNWACvAlkavfGhKnojDwpZEPoTXBNXYCLNzNIkmVgkgp33pw2TxGzPk93DbLO65J1zARdWZFGMmS6HRA7jb30wHXvQ5pkdERgHjgd3pDC9Dkiz5UaVUYmFLqlpt7suM5Dg0NklKTerOb8Et7J3vzwm4cPxn7e12seL+3blDqyePsHnES16bcWYpNDYAx4vIccALwOVYZdfN/cDVwGPAXwG/qXl/Rp3e6EmSZD5EbledOQ+NTRo/P5Hfb+GZZ/wDLsaNs+/bvdv6PRzCaqtRcnjy3IwzM6FhjDkkIjcCD2FziG83xmwVkS8BncaY+4GVwPdFZBtWw7g8q/GmQp3f6EnhZ+ceNcr6OLxWo+Xc5Gk5sstaeea4HliW+P0W3vpW74CLhgbbdVBkZELf2rXB2mrUOlhhtJ6syDS5zxjzAPBA0bYvuJ73AR9Ke1yZUe6NrmasUHiZkY47Dh56aOTNnNdaUH4rT18Bl/PQ2Kwo1St+5Up7jZuarMC47TZ47jn/ciGDg/DKKzBtmvf+qGbRSsLM00AzwvNEOTe6mrHKwm1GGjXKCozim3nChHx26vNbef7FX9hJzVPA5Tw0NktmzRoySb31rVZgwMiAi4YGWL06uMaUH1HNopWGmSeNFizME2EL/7nNWP2Fx9/iXzwup8Xl0sbJ1j10yLuo3a5d8XXqizO811l5umlqGhJ8vpnoOQ6Nhew64XV0wAknwIc+ZB87Oob2tbbCggX20cuU5UZkqHSIF1FzOuIMM08C1TTyRpjCf15mLAH+DMwseo9qJCMYNWrk6nFw0AqUOBK34jZx+a08p04dvk3EYxWb09DYrMyA5fgLmpvhpJPgySdHnkcEzjyztMZQSXRdngs5qNDII0E3upcZ6xDwKPBfDAmGOnas+9n6ncnKqR/U0GCfL1wI48f73+RhneNJhPc6K89ie3tz83Dhd+iQtbEnHQpaKUmFQIehXH/B7NmwdevwxURDA1x4of29BFFJdF1eCzmo0KhG3PZqwQoMsKYqGBIMdRpB47eK9apLBHDBBUMTgNdNXs6qOKnwXq+VZ1cXdHYOP27TJpg+PUehvx5kWRK+rW1kVduDB/39Bc3NcMYZIxcSYQSG+xx5/j7KRYVGteKYsf6M1TD6XfscwVCHETSlVrFek1Vj48hQSvdNXu6qOMnaRMUrz0mTrKnNPf5q6MeRZf2mceNsFdvbbrPf/cCAfT2uxD2R21ycjFChUc20YH0Y/1W03REMdRhBU2oVG2Wy8nOC+03M5dixK+0NPmbM8DIYUB3F85LOpC91Xffts1VtZ8ywFYpnz7ZaRpCgrTVtoRJUaFQ7QYIh4Y56eaOUYIgyWXk5zQcGhrf9LCbMyjQOR3Duy5iUIKnVe9B1HTMGfve74ZrGddfBJZfE8/n1gAqNWiBIMOQ0giYJgibScicrJzy32BFabNLyGoffueN0BFez6STK6r2UFhHmur7+OqxYMTwabcUK+Lu/q65rlyUqNGqFOhIMQQRNpOVMVmPGjOzUJlKZCShuR3C9mE6CtIgw17W7G444YnhuyOjR+cm2rgY0uU+pSeJqu5lE/4SaauSTQuJoXx9s3z68TatXMmOY6+oVPZWnbOtqQDWNpMlrfWMlNHGbgPLgi4jlZ5lC4qijXcBI31KxFhHmuq5aNVywNDXlK9vaTaWBEkmhQiNJ8lzfWAmk+KaN88bN0hcRy88yhcRRv7waBy/trNR1dbLB3f6MhgY49dR4xhsneS2cCWqeSg53vYI9e+xje7vdrmRKmJpH3d22JPbq1fbxT3+y23t7YcOGeL5Gx4QG6dVgiu1n6SSOunHyg2LCr52uu02rX9izl2nSq4bXgQMwd+7w+lNZ4xaWvnXFMkQ1jaTIe33jOiXMCs4vCmfNGrj++ngVR6/xJKmBdHePDBeO9LNMIXHUy0fR0GDzLCZOLP/aeNXwAis48tSvIsuM+TCoppEUea9vXIeEXcF5rXBff90KjFIr9HKrtnqN57HHvDWcuNi0yf4vbiL9LMNWZK4AryCEM86w/SuiTJ5ODa8jjhi5zxGceSDvgRKqaSSFV5W5vHrc6oSwKzivm3bHDvs1+imOUWzQXuMxJrlCfr298Ld/O3L7v/6r988y0BGbQuJo3L6fpUutD2Pu3OFRVMWCM0sndB4CJUqhQiNJ8lzfuA4Ju4JrboY3v9kWBHRYsMBOLG6ciSZqsl5QvwaI1yzhZTEdOxbmzfM+NpQQTCE/qDgIodIJ/cQT4Xvf81/P5cEJneekTRUaSZPX+sZ1SNgVXF8f/PGPw7e98gp89avwiU+MnGic5k3l2qCLxzMwYDUNdz2pOM0SXhbTgYGRpikvIbh2re1sWE511ySIa0L3W89lWba9mLwmbarQUOoKrxVccc6Cl9loYMBG5Nx6Kxx9tG3A40w0ldig3ePZvdv6HByh4fT5iGviCGsx9fr/BwfhwQetTyGr0M+4J3Sv9VwaTui85l+ERYWGUne4V3BeOQsXXeSdGzAwYG/0vr7hpbQrtUE7x/361yNrXBV356uUMBZTP7PZ4GC2PdPTmNCTdkLnwfRVKSo0lLrFq/Xnxz4G//ZvQ0KhuFihs614oqrUBp1mmGWQxdQRgmvXhvvf0yKNqKIkndB5Mn1VggoNpW7xcgyLwEsvDU1ExowUHH4TVSU26LyFWc6aZX0YDz4Y7n9PA/eELmLHMm9e/BNuUk7ovOdfhEXzNJS6xc8x7F6FjxoFJ58cb8FCL5IojFgp48dbH0aexjRrlg2XHRy0E+6mTfHnskB8BS/d5G1hEBXVNJS6xcsxfO21cNRRQ8cMDtrubrNnJ++8zGOYZd7G1NcHjz9uvxdnAq4WE0/e8y/CokJDqWuKHcNvvOF/U6dxc1caZplEUeU8hX5Wu4knb0I4Cio0lLrHcQz39dkJ6IILbGe+arupsyiq7BZS48ZFmwz7+mweDATXlKoFE0+ehHAUVGgoCt6hkE4F2mrAKxIs6SJ8biF14AAsW2aLCZYTStrdbaO0nNyUhobSuSC1YuKpZlRoKHVPLYRCpl1U2UtI3XYbnHSS9QmFuX59ffa44gz4oPfWgomnmtHoKaXu8apq69jJq4W0iyp79aZobByq+hvm+u3bN7L/untfKZKIbgpDuZWMa5FMhIaITBKRX4lIV+Fxos9xAyKyufB3f9rjVOqDuOzkWU4ora2wfLmdyFta7ONXvpKcaSooXDnM9RszZriWUbyvmKwnbL/GXPVGVuapW4BfG2OWi8gthdef8ThuvzEmh80YlVoiDjt52OZOSZlU+vrgTW+ytbF6e+3kPXGi3Z7Earw4XPngQevTmDw5/PVrbrb+i8ceG+7T8Hpv1uU3asGEGRdZCY1LgXMKz+8E1uAtNBQlFSqxk4eZUIImvUpDZR0T21FHDeWZJB2KWhyuHCV6yrnupaKngq5vGgUAqz3UN06yEhpTjTHbAYwx20XkaJ/jmkWkEzgELDfG3JvaCJW6I2ooZNCEEjTpxREqm1UoanEdqyjXr7nZduPzo9T1femldDSQWgj1jYvEfBoiskpEtnj8XVrGaWYaY+YDVwBfF5G3+HzWMhHpFJHOXnf/TUVJgaAJpZSj3R2F5NdGNgx5LEPiUKkvwu/6jhoVrn1vHOT5+qZNYpqGMeY8v30iskNEphW0jGnATp9zvFh4/KOIrAHmAs95HLcCWAEwf/58H9eaoiRDkE+klFB58sn4QmVLmdiy6uEQhy/C7/oeOjSkgbz2mhW006YlZzLSUF9LVuap+4GrgeWFx/uKDyhEVL1hjDkgIlOAdwD/nOooFSUkpSaUUkIl7lBZLxNbHBN3FKETp/PY6/r29dn/59FHYcUKu/ofGLBC96qryjt/WKo9mzsOshIay4Efi0g78GfgQwAiMh+4zhjzceBE4P+KyCDWjLbcGPNURuNVlEBKTSjFkx5Yk824cd7d9MaNs/srXdHGMXH7CZ0gQRK387j4+jY320KSH/nIcMF73XWweLF2WU6KTISGMWYXcK7H9k7g44XnjwJzUh6aoiSGM+kVT8JnnWVj/p0opH37bB6As3/ePBtVFHdzJ2d/qfP6CZ2DB2212VLaSxrO44EBO3a30EgyE17RMiKKkip+k/CSJbBggd2/evXw/Rs2WKevMeFMS24NwG/i3r3btpd1mhmddppdtRfjJXREbB+LwcHS2ksadaLa2qx25ibJTHhFhYaipEqQycZrP1inLwSblrxMScUT97x5dtJ3f8aGDVYoHX/88PP5CZ3iboZ+ZqekncdePVFWrlQtI0lUaChKigSZbLz2uynlEyilxSxZMjRx+9V82rQJZswI1hbmzrWmKb//oZikncfFSYYqMJJFhYaipEiQyaa4D7ajYTiUmpz9tJRXXrGhqO6J20swlaMtjB6dr/LkxUmGSnKo0FCUlAky2bj379490uHsNzl7aSkDA/DII7bP+ezZQ6v+006zJik3xoTXFjRnoX5RoaEoGRBksnH2T55sTUZhJmdHS1m7drjwGBy0SYRbtw41OJo92wqJTZusQHKc7OVM/pqzUJ+o0FCUnFPO5DxrljUd/e53I81Ur74K//EfNq9hxgzr9A4rkBTFQZswKUqNMdGjO82jj8JNN8GXvwwnnGCLJEJ2zYyU6kWFhqLUGO7iemDrMq1YYRPg3ngjelFERQEVGopSk8yaZcNs58yx5UhGFRminazpWiHrrn71hPo0FKVGaW6GU06B8ePhH/9x+L5ayprOuqtfvaGahqLUODNm2Czplhbb1a+lpXaypt0JjUn31FAsqmkoSh2QVdZ00n08tA1r+qjQUJQ6Ie2s6TTMRtqGNX3UPKUoSuykZTbSNqzpo5qGoiixk6bZSEuapIsKDUVRYidts5GWNEkPNU8pSp3S22uLFiaR5Kdmo9pFNQ1FSZmkI4rC0NFhs8JHj7aZ4itX2girOCllNsrDNVCiIcaYrMcQK/PnzzednZ1ZD0NRPMlDIlpvr/3M/fuHtrW02D7l5URXRZ3483ANlJGIyEZjzPyg41TTUJSU8OusV6p9axJ0d1sNwy00nLIiYYVG1Ik/L9dAiY76NBQlJZyIIjdORFGatLVZk5SbcsqKVBJOm5droERHhYaipEReEtFaWysrK1LJxJ+Xa6BER4WGoqREniKKli61PoxVq+xjOU7wSib+PF0DJRrq01CUFMlTIlrUsiLOxL9uXbje5cXk6Roo5aNCQ1FSphYS0Sqd+GvhGtQrKjQURYmETvz1ifo0FEVRlNCo0FAURVFCo0JDURRFCU0mQkNEPiQiW0VkUER809ZF5EIReVZEtonILWmOUVEURRlJVprGFuADwCN+B4hII/BNYDFwErBURE5KZ3iKoiiKF5lETxljngYQkVKHnQ5sM8b8sXDsD4FLgacSH6CiKIriSZ59GscCz7te9xS2KYqiKBmRmKYhIquAN3ns+pwx5r4wp/DY5lnHXUSWAcsKL/eKyLPhRhmKKcDLMZ4vTvI6tryOC3RsUdGxRSOvY/MaV6gC9YkJDWPMeRWeogeY4Xo9HXjR57NWACsq/DxPRKQzTI35LMjr2PI6LtCxRUXHFo28jq2SceXZPLUBOF5EjhOR0cDlwP0Zj0lRFKWuySrk9v0i0gOcCfxCRB4qbD9GRB4AMMYcAm4EHgKeBn5sjNmaxXgVRVEUS1bRU/cA93hsfxG4yPX6AeCBFIfmRSJmr5jI69jyOi7QsUVFxxaNvI4t8rhqrke4oiiKkhx59mkoiqIoOUOFRhFllDjpFpEnRWSziHTmbGypll8RkUki8isR6So8TvQ5bqBwvTaLSKJBDUHXQESOEJEfFfavE5G2JMdT5tiuEZFe17X6eErjul1EdorIFp/9IiK3Fsb9BxGZl8a4Qo7tHBHZ47pmX0hpXDNEZLWIPF24N2/2OCaT6xZybOVfN2OM/rn+gBOBE4A1wPwSx3UDU/I2NqAReA54MzAaeAI4KeFx/TNwS+H5LcBXfI7bm9J1CrwGwP8Ebis8vxz4UY7Gdg3wjTR/W4XPfRcwD9jis/8i4JfYHKozgHU5Gts5wH9mcM2mAfMKz8cB/+3xfWZy3UKOrezrpppGEcaYp40xcSYHxkbIsR0uv2KMOQg45VeS5FLgzsLzO4H3Jfx5QYS5Bu4x3w2cKwF1bVIcWyYYYx4Bdpc45FLgLmNZC0wQkWk5GVsmGGO2G2M2FZ6/jo30LK5ckcl1Czm2slGhER0DPCwiGwsZ6Xkhi/IrU40x28H+UIGjfY5rFpFOEVkrIkkKljDX4PAxxoZ37wEmJzimcsYG8MGCKeNuEZnhsT8L8l7a50wReUJEfikiJ6f94QUT51xgXdGuzK9bibFBmdetLtu9xlDiBOAdxpgXReRo4Fci8kxhNZT12EKXXymHUuMq4zQzC9fszcBvRORJY8xzlY7NgzDXIJHrFIIwn/tzoMMYc0BErsNqRH+Z+MiCyeqahWETMMsYs1dELgLuBY5P68NFZCzwU+B/GWNeK97t8ZbUrlvA2Mq+bnUpNEzlJU4wNqcEY8xOEbkHa3aoWGjEMLbQ5VfKodS4RGSHiEwzxmwvqN07fc7hXLM/isga7MonCaER5ho4x/SIyChgPOmYPwLHZozZ5Xr5HeArKYwrDIn8tuLAPRkaYx4QkW+JyBRjTOJ1n0SkCTsp/4cx5mceh2R23YLGFuW6qXkqAiIyRkTGOc+B87E9QvJAFuVX7geuLjy/GhihEYnIRBE5ovB8CvAOkitzH+YauMf8V8BvTMEzmDCBYyuydy/B2qLzwP3AVYVooDOAPY5ZMmtE5E2OT0pETsfObbtKvyuWzxVgJfC0MeZrPodlct3CjC3SdUvDi19Nf8D7sSuDA8AO4KHC9mOABwrP34yNenkC2Io1HeVibIXXF2EjJZ5LY2xYX8Cvga7C46TC9vnAdwvPzwKeLFyzJ4H2hMc04hoAXwKWFJ43Az8BtgHrgTen+BsLGts/FX5XTwCrgbemNK4OYDvQX/idtQPXAdcV9gu2Mdpzhe/QN7owg7Hd6Lpma4GzUhrXO7Gmpj8Amwt/F+XhuoUcW9nXTTPCFUVRlNCoeUpRFEUJjQoNRVEUJTQqNBRFUZTQqNBQFEVRQqNCQ1EURQmNCg1FURQlNCo0FEVRlNCo0FCUhBGRBYXig82FagJbReSUrMelKFHQ5D5FSQER+UdsFnoL0GOM+aeMh6QokVChoSgpUKgztQHow5ZqGMh4SIoSCTVPKUo6TALGYjuoNWc8FkWJjGoaipICYnui/xA4DphmjLkx4yEpSiTqsp+GoqSJiFwFHDLG/EBEGoFHReQvjTG/yXpsilIuqmkoiqIooVGfhqIoihIaFRqKoihKaFRoKIqiKKFRoaEoiqKERoWGoiiKEhoVGoqiKEpoVGgoiqIooVGhoSiKooTm/wOPAXHKS4gxPwAAAABJRU5ErkJggg==\n", 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WpnvunjgRZWHopDl7XacVsVfrOlmENe1dWn0MY2ZKK1qwyQgTfdWqFKZxiEg78A1gGbZLwVYRWW+Medqz6XeMMdflPsCiCFpxpxVx5NZYai2rvO9N7bLCyk0LOk4TUcsU9wJjfRNQ+/jW0syimFKHsAmJ6hRXIlCkqeosYLsx5gUAEfk2cBngFRyth9+Nn4b/w9eG3hW8pOryvNfM/oe88Jvwh4DNPtueS+3jm9TX5r0e2qgf+aYoFCs4jgd2uv4fAM722e5DIvJe4N+B/26M2emzTfMzjfHVVaOsCtPQWJrZ/5An3gnfb1EwAeuzyCp92e96EGBpdb96bpUaFOnjEJ/XvLHB/wh0G2PeiQ00vMv3i0RWi0iviPQODg6mPMyMiFpwbsDzvxBtVZhm1nuT+B+Gh2HvXvvofh6buEUE/XwfBvinDNOX/a6HdmzWfxOc26ikcv6bYAxhKVLjGABOcP0/D9jl3sAYs9f17x3ALX5fZIxZA6wBm8eR7jAzIGqSn1+vizbs6j8smtw1BncznUOHQATa2xM01kmSuOnn+zh1EC7IMH1Zr4e3KENjpTKMIQpFahxbgRNFZKGITAQ+Aqx3byAic13/rsCmJzU2ceoBpaEtlKk+VsG4m+mMjIAx9mYdGbGvbdkScdWXRo0nbzRUW7/NpXHjzrNJil4PwPhrIdb5b4IxRKUwjcMYc0hErgPux162dxpjfiUifwL0GmPWA9eLyAps0YqXgauLGm9qxHFyp7U6LLGPIs8ezH7NdNxEbqyTVuKm2/dRL88mDUp8PeRFGRorlWEMUSk0AdAYswHY4Hnty67nNwI35jqorAsMxi3HnVZEU1ZZ7wnIW02fMCFYaECMxjpZmH3ySl8u4fWQJ2VorFSGMURFM8fd5FFgMK4QaNLVYd49mB0h5eD4Nbw+jkj7zqpUiqYvZ04ZGiuVYQxRUcHhkHZJ71qaS1wh0ISrwzzVdLeQcjAGli+HSZMSmsqyEuzeXJokZBXa2+CUobFS0jHkaeoFFRxHSLPAYBjNJQsh0Aid8zzkqab7Can2dhtVNX16CjdcVoI9jfNaq4R+GjS4UKpUil/hxx1DERFZ2gHQYZBoJR+CKKqLWqN0zvPgqOnt7daU396enZruJ6RGR63Po7REPa9+uSTuzkT79tnHnh77er3PhiHrdnlNSFo5G0VFZJX5lsmPuCUf/CiiGVGjdc7z4FbTJ0ywGsDwcPrCw21LhiOax/33lyRu3qtZRD2vQZpuvc5ETh+OJ3w+W48WbZeXxDSUpoZQVESWCg6oXfIhKkUkVjVi5zwPlQr8+tf1b6ikttwFC+Doo+EnP7H/OxpIlg75UPhN+kcR/rzWEjK1Qnv7qts512zUhUeDtMtL0weQZOJPOxikqIgsNVVBcMmHOJN9EYlV9YRVXBNEjoRRufv7Yf16ePBB++g0K4zKoUPWJObGWaUVQpB5s4Pw57VWkmhQCf2pXXa/+wahfyvsHxz/2XrkkW+SkKDrJo65KKlpyNEQ3CS59vI09bpRjQPSD6fMO3S21vjz7GGegHoqd5ortbCrtNz8vUEa4wjhz+u51BYyfqG9e4DH1sHf9UD7RBg9CKvWwlkrw2vJJW+XF3TdHDwITzwRXWtIahqKoiGE1ZKKiApTweGQ9mSfd+jsIhieBW/8BqbMgsrRhLORlyQSq94NlaYtN0zcvDcI6Wtfg499LKObspbGOBv/Muze8/ooVng8SvDixxvaOzxohcbIkP0DuLsHTl0Kv9cV/noocb6J33UjAo8/bs+7W5hMnAgzZtQ+x0lNQ2FzNqKaw/KOClPB4aaB8yR8L7ROatvIS6SN1Luh0rbl1lql+fl7r7vOTjhO4FCquDVG4YgG0el6v14Z9jasT+5Kwi8EXuqHSROPCA2AiR3wrn7bpyUKaeabpEjQdeNcYw6jo/Dww/Z5rUk6jWS9ehpC3kmxcVDBUQYSrvoDL7T3QyVoJVvCSKxaN1QW2bVBqzQ/f297u3XeZ3YDL8J24tuM9Ys9CkzEX5DX0lCiLH66u615asz3jMDJ3SG/oPz4XTdnnGHNVF7CTtJpmIb8rj3HNHXwYPlrV6ngKJoUVv2BZpxRqLwP2MSRieYw8CLRInZypJbKnZct18/fOzpqF9SZ3cBOSHgYQZ6WT67k/om08LtuJk4cH5btEOYcp20acpfCMcb+uSlb7SoVHEWS0qq/phlnsmdjU93H5TRkP4Y8bLnu+RTsxLJ6tQ1IOnQo4g0cVpuMGlKdlk+uxP6JNPFeN44weeUVeOihsfdP3pP08LAVYu4xiFgBFrt+Wsao4CiSlPIvappx9nCkj7R7H7UidhRWroT3vAe+9S2YOdMKDbA3dGiiaJNxqyancb5K6p/ImkrFOsPf8Q54+uniCgy+8sr4hZ8xcO65MHVqcfWzaqGCo0iSJgu64kUXLOjyN+NEjdhpEPIo6jZ5Mvz2b1sLjkN7e0hTVVRtMqsKu7XG14DnPU3cASXGwMknw6JF5ZmkJ060i5YyooKjSJJMFj5F6yorV46/6OvtowEjyZwbfv9+eOkluPhiWLw4/f0kiuSKo03mlf9Tomi6PPBbZPgFlDz9tBUceTNjhtVk3X4NEft6WVHBUTRxJoug+kDvXgqVrvHf00S9PJwb/uGHYc0aqwHcdBPccQesWpXuvhJFcsXVJrMW5CWMpsuSoHyIMnXdq1SsWWrz5iMC5JxzyqP5+KGCowxEnSx840U7YE0//FaX/yqyATULP954w2oaa9aMjXy69lrbVyNtU33sSK4k2mSWKetNUNcsLLXyIcrWda8MPUGioLWqGhG/eNEDIzCjO99S7jFJUlJ6yhRrnvLWmnLq6qW1HzeVirU1R76ZF2ET8i6tPoYxg2RdojxIE+qg9PXMolKrLlRRNZ5qEfs6KwDVONIkr+JGXV1w21q4tsdm+h4csTWGprn2WdJVZNKS0pWK9WncdNPY17119YpobuNLFE0vjxLlfprQScAPaUifR60giXpaRaOt8stEXY1DRK4TkRK7aUpCVitFv8q2fcChlXDzDvj0RvjnHbYwnZsS5mSk1XRm8WLr0/AWe3Xm1ij7GRyErVvH9zQqBMcE6carSqVR6ditCV0OPEf+jcdSoF615DBaRSOt8stEGI3jWGCriDwO3Ancb4w3r7HFyWql6Bf9cjxHnJudXTC/y9749QrclYA0HZKrVlmfhp+CF3Y/WXdTjUy9EuVpRkM5mtAeGtLnEbaeUyNrFXn3EY9CXY3DGPM/gROBtcDVQJ+I/JmI/FbGY2scwqwUoxLUo+E3+PddcArcRbGn50zaDsmuLjjzzPGyOcx+wnZTzZWgvhlOlz6/6yGpZlBE47EUiNLXohG1irR6z2RFKOd4VcP4dfXvEDAD+L6IfCXDsZUbt8kgi2Y2QY15DjJ2dQhjC9zNprQrxbwckmH2k4WsT4WVK+0ssXGjfXRUoFqNmpJQROOxFCgiKiqtYIsw+ymij3gU6pqqROR64CrsWvdvgc8bY0ZEpA2rPH8h2yGWkHEmgwyKxfmtBEeBB7Glt6nuX2iIG90hL9NBvf3Uk/W5NXHyw68ESJaaQQPm+WRRLbkWeQZblCnHJIgwPo5ZwOXGmDHKkjHmsIj8XjbDKjFBCVRXplwszi/65TC2SKGDYJ2bDRa6kFfTmVr7qVUYNrbvI8syHlmXJGnAPJ+8FiF598coW46JH9Jsfu4lS5aY3t7e7HawB/gx1s7s0IH1LczOYH/OZHQA2Fhjv1p7KBZezWJw0E5I7tzKzk5rNaq5FsirjIee59zZu9f6Gtw1yzo64IILsqsltWPHeG0q63ByEdlmjFkSZlvN44hK3s5EZyU4VGO/LVZ7KE28ViG/pHzH9xEoOPIs49GAmkGjU4QGUPZoMM0cj0pRzsSg/UI20TYtSqw4h6wc14oveTmpHYrKMq8VDZb3MfCiGkccinIm+u23QePwy0qspngNGtLaiBRVEaBMGkAZqiKo4IhLUSYD736bfNIqIgkqclO8vHtptCh5O6m9BAVbRInAS3o9F30MHFRwNDpNPGkVubKK3BSvAUNaG40yhqlGicBL43ouyzFQwdEMNOGkVZaVVSTUcZ0pZQtTjVJpKK3ruSzHoFDnuIhcLCLPich2EbnB5/1JIvKd6vtbRKQ7/1E2CCXPGq+H19kXpaREVmNQykXZSqFHqT6Q1vVclmNQmMYhIu3AN4BlwAC2kOJ6Y8zTrs16gFeMMYtE5CPALcB/zW2QGjOfC34q/Jw56ayswtqUy+BwbCgKujfK5KSOEoGXpqZQhmNQpMZxFrDdGPOCMeYg8G3gMs82lwF3VZ9/H7hQRIQ86APuwSb73QNsz2WvLUdQXR5IvrIKWyiuEWoDlYqC742yFC2sVZPSjbN4OeOM9DSFoo9BkT6O44Gdrv8HgLODtjHGHBKRfcBMbN2s7GixvsxFUsvZl2RlFcWmXBaHY0Og98YY6kXgeTXZxYthxozitaWkFKlx+GkO3vonYbZBRFaLSK+I9A6mURdbE7pyo54KH3dlFcWmXBaHY0Og98Y4gsr7+2myjz/e+EIDihUcA8AJrv/nAbuCthGRCcB04GXvFxlj1hhjlhhjlnSlUcq0rLkRpWpXlw5ZOfuiCIOyOByTkJtjv6z3RgkpIsAjL4o0VW0FThSRhcCLwEewLYjcrMeWdH8U+APgZ7l0HyxjbkTp2tWlRxbOvrBlt93JWytWlMPpGpVcHftlvDdKSjNrsoVWxxWRS4CvYisv3WmM+VMR+ROg1xizXkQqwLeAM7CaxkeMMS/U+s5Uq+OWJaoqdslWpVZUVTPI4uFh6/h3+2fa260QzFT4leXeKDlFVLmNS8NUxzXGbAA2eF77suv5MPDhvMf1FvUSuvK6eWKVbFWgdpmILNrE501hjn1NdgxFGUJns0Azx+OSZynzLFrTtjhRZXERNbPC0MzmkGYhr8ZleaJl1ePgDknMo5R52IBx5S3qOYujyOK+PrjvPvjRj+DWW21kTNrjiUsejn3NqFe8qMYRByckMc9S5pFLtrYuYZzFYcun9/VBby888gisWWMn5ptugjvugFWr0htPErI0h2hGfXwK7VufMapxxKGokMSggHHlLaJkga9caZ2XGzfaR69jfHjYahevvWaFxsGD1rR18CBce224qOi8stLH5bukELqtGfXxWbfOCthly+zjunVFjyhdVHDEIWwXwCFso6Uh8s3BcO+3xfCLnReBXbv8J7xasviNN+xnBwetpuEmqJhdmPFkHsuf0qyV1didW2HnzsYwgUU11bkDL/bts489PWNv/UY3/6mpKi71Spm7neeProNv9cCkHOI+W7z/uJ+z+NAh2LbNmpyimFpeftmusru6xkYtQfjYhNyd1zHCxRzH/4QJ9lg55q4sxu6EQE+YAAcOwCc/CeedV14TWBxTXb3Ai2Yw/6nGkYSgUuZu5/nLg/D3PTBcY/mRFnk77TMmzqrM7Sye4FoWHToUzdQyPAxPPGGfH3UUrF5tJ4OpU6PFJuSelR6l1jdHCkFu3AgbNsBPf3qkIGTaY3fLtP377RrqttvglVf8z0vRq/K4prpagRfNYv5TjSML3M7zvf3QPhFGcsjBKMJpnxFJVmWOs3jXLqtpHDp05L2wOQ7e/IjzzoPTT4f58+1jlFOXayx/hHAx9yTm4C0ImebY/Vbi7e1WoMycOfa85LEqrxdiHTdHplbgxd69zVFQUwVHFrid5zO7YTSnHIwmqSOURre0SgWOO86ap9yMjo7VRILwM9NMmwZnnWUF0fBwtBs9t1h+z6w1eHA6/f/jDrrpwivr/CZGB/dkltbY/WSaYwp0m8Dqnf96E36YnJswgimJqS4oCLJZ8m7UVJUFbuf5MV1w9Vqo5JCDEdZpX3LS7Ja2cOH41++/P7g3h/uzXjPN295mP1uvv0fhVMPF1n3+cRbIDpb9n4t8feR+k5hDFpOZOx1p2jSrfXzyk7bMuNsEVuv81+uxEqYHS1hzUVJTnV/gRTMU1ATVOLLD7Tz/yEr405xyMJqg/3haq7LhYfiP/xj/PRBOg3GbaSZMsEKjbD3Qh4fhzR2DTBnsZ9JJ3W9dW4N00fNnXTV95O5CkMbYY9PebsPi2bzXAAAXtUlEQVSPZ8+2fgjvb0uaQe9eiR97LEyePP67gs7/hAn1NZEwmmoUE1QWZsZmKEOigiNL3PV8Orvyy79o8DpCYSvb1iOsKabeWCqVctqm+/th91+u48zbezjcPpHD5iBtd9qIvbAlVbzC8Tvfgc9+1r/wY1p+h646t0LQ+T90qPY5CCsQ3ILptdesj+XYY4MXJlmYGRu9DIkKDqWUpLEqS9MUUzbb9PAw/GLjIJfe3sOEg0M4oXOmpwdZupTu7q7QJVWcSWxw0AoNPy1l2rTkfqco+J3/4eHa5yDsOXIE0623wu23Wy3LGLtdo1VHLgr1cSilJWlfZbc92bGZt7fXtisHhYCWzTb9xhsw9Tf9HG4fG3prJli1Ik55s1qRvFklA9YKufWe/3rnIMo5mjzZlo1xqgEMDyeLki86dDhvVONQmhqvKcad4OalnimmTLbpKVPg9VndtHki9uTQEbUianmzWpG8WWhccUxf9c5B2HOUZqeCZkjoi4pqHErT46xcp08P1mCiRNok0YLSolKB05d1sfXatRya2MnBzqM4XOlEPGpFlPJmtbSUtDWuJIlw9c5BmHOUVqeCZknoi4pqHErDkHZPDHf10ra28jm/67FgAcy5ZSWv/eFSpgz201aNqkpynGppKWlqXIU1oKoStjpyPYr+HUXREoJjZGSEgYEBhpt9GRCRSqXCvHnz6OjoKHoodUnbHOBtG3v77eNv9LInZh0RfF1MOsnOeGkcp1pRT2lFA5Uh2CCNTgVl+B1F0BKCY2BggGnTptHd3Y2IFD2cUmCMYe/evQwMDLDQL0uuRCTNJPeuwP3qAF57LfzLv8D27clCgPPCr1/6Bz+Yb+RTEsKEXOfRdbFeaHA90godbzRaQnAMDw+r0PAgIsycOZPBPMq8JySJOcBvBb5nj79jdHQUVqxIb7LKauILKoC7eHFjmU1qmb4ayeFcpqCJvGgJwQGo0PChUY5JXHNAkKZy7rnBjtG0TDFZTnxBEUGDg+OPU9jaXEXhd7xraZj795ezq16jJ/RFRaOqlNITN6InKPdg8uRsW7hnHWkTFBF00klHjpO78VSY2lxlIui83X13c3fVayRUcOTEq6++yje/+c3In7vkkkt49dVXI3/u5ZdfZtmyZZx44oksW7aMV155JfJ3lIkFC6wZ6YIL7GOY1XstTaVe29gkpJ0s500uqxU2u2ABXHSRzYQG+3vrCa64yWtZJb35nbdXXz2S1Z51WxulPio4ciJIcIz6FVJysWHDBo4++ujI+7v55pu58MIL6evr48ILL+Tmm2+O/B1lI2oORT1NJasW7mlG2gRVe60l+JyaTm6CBFeYarJ+9PXBfffBz36WfqVgv/M2e3ak/lRKxpTY+lkwQ6RaYfaGG27g+eef5/TTT6ejo4OpU6cyd+5cnnzySZ5++mk+8IEPsHPnToaHh/nMZz7D6tWrAeju7qa3t5fXX3+d5cuX8573vIdHHnmE448/nvvuu4/OTv/B3XfffWzatAmAq666ivPPP59bbrkl+Q9pMNJyXEZxdKcVaVMvmiwoIujll8c2rwJ/wRU3Wq2v70ifkyjVhqPgPW9Ox0A3WbW1UeqjgsOPDPp233zzzTz11FM8+eSTbNq0iUsvvZSnnnrqrVDYO++8k2OOOYahoSHOPPNMPvShDzFz5syxw+rrY926ddxxxx1cccUV/OAHP+CjH/2o7/5eeukl5s6dC8DcuXPZs2dPsh/QwCR1XDqObhE7US5eDCeeWPszaQisONFk7pa3bhYvHv+ZuN//+OPjXxdJP3rLfd4qlXQS9pR0UMHhxd2327mhfo7tcZFiqfKzzjprTP7E17/+de69914Adu7cSV9f3zjBsXDhQk4//XQA3vWud9Gvenrm+LVX7e21E+W8ebUFQ1KBFcfk5ScMJkywzZLS/P4ikt7SSNhT0kEFh5ec+nZPcd1lmzZtYuPGjTz66KNMnjyZ888/3zfLfdKkSW89b29vZ8gdj+lhzpw57N69m7lz57J7925mz56d3uBbiDfesELCS2+v7Wfe3p5dnkEck5efMHBKhqf1/Y7j3c273jX+c+6SLmEm+eFhcGI4ZszwH0fShD0lHVRweMmob/e0adPYv3+/73v79u1jxowZTJ48mWeffZbNmzcn2xmwYsUK7rrrLm644QbuuusuLrvsssTf2YoE9fQw5kjXPMguQzuqySuqMEjy/W7T3SKPKdfJbJ8wwfomvvY1+MQngr+3v99+p3M8RWy+TVmT/lodFRxenL7dP2esjyOhtjFz5kze/e53c+qpp9LZ2cmcOXPeeu/iiy/m9ttv553vfCcnnXQS55xzTrKdYZ3xV1xxBWvXrmX+/Pl873vfS/ydrUilYidGxxkcRJYZ2lFNXnGEQZrf785sd7j2WvvoJzwcc6BbQBsDmzeXs1yKAmL89M4GZsmSJabXc5c/88wznHLKKdG+KOWoqrIS69i0INu3W9NUW9sRTcN967S32/ySVp/khofh4Yfh8svh9dfHvjdpEuzcOd7UtHcv/PSn41v8trfDhRfaEGwle0RkmzFmSZhtVeMIosH7divpsmjRWGf4Sy+1XmG7ejjRZ/v3+ycFTpzo3ygpyKke5Jtx+06mTWutGlFlQQVHg/OpT32Kf/3Xfx3z2mc+8xk+/vGPFzSi5sVt0mnFwna1cEefTZ4MV11lw2XdHDoU3PP87LOtacrt4zjnnPHH1V0V+MABWL0afud3yl8IsdkoRHCIyDHAd4BuoB+4whgzriaGiIwCv6z++5/GmBV5jbFR+MY3vlH0EFqWIgrb5VFqPA7eMOALLzxSX2riRCs0auVdOIK4VlSVX1Xg22+Ht7/dll4pawn5ZqQojeMG4KfGmJtF5Ibq/1/02W7IGHN6vkNTlPwJIxDKXGrcL/rsggvsRN/WFi4kt1KBas6qL35VgdvbrUA56qhyl5BvNoqqVXUZcFf1+V3ABwoah6IUTph6UWlX3B0chK1b6xcJDFvI0Ik+87JjB5x2Wjq5F35VgUdHj3x3K3TeKwtFCY45xpjdANXHoOy0ioj0ishmEQkULiKyurpdbyM0JlIah6wqwLq/P4xASLPi7rp14cqTRy2AOGPG+N4fSaoCe/FWBa5UbJjvzJnhS+0r6ZCZqUpENgLH+rz1pQhfM98Ys0tE3gb8TER+aYx53ruRMWYNsAZsOG6sASuKhzxMQ2HrRaVVcTeoe+DSpWO1gjgFEP2yytPWArxlR4KiqsrqC2oWMtM4jDFLjTGn+vzdB7wkInMBqo++FfiMMbuqjy8Am4Azshpv1uTdj+N73/se73jHO2hra8Ob16LUJ+tmTI6p6M03wwmEuM2svDh+Ajd+5cnjaDjeMTrCL6BgQmzc5fD9Su3HLRWvhKcoU9V64Krq86uA+7wbiMgMEZlUfT4LeDfwdF4DTNtEkXc/jlNPPZUf/vCHvPe97438WSX9Zkxu3Kaik06yOSFhBEKcZlZegroHesNk42o4zhhffhmuvx5WrUrerS/KvZi1wFcsRUVV3Qx8V0R6gP8EPgwgIkuAa40x1wCnAH8jIoexAu5mY0wugiMLE0Xe/Tg0GzwZaTZjcrNz53hT0Re/CM89Z/Mf6plWkoYAO36CeuXJk/QU2b//SLe+WuawMES9F+OUileiU4jgMMbsBS70eb0XuKb6/BHgtJyHFru5TT3y7sehJCOtZkxu+vvhnnvGV9vt6IBf/9qaX/IgbHnyuEmOfmGzjjksiuCI62cpouR7q6GZ4x7yWrFoP47yk2Z2uDMJzpw5vjtfEZ3swpYnj6PhhDWH1SPOvRhX4EctAd/qqODwkNeKJet+HEo6pJUd7kyCRx1ly2SsWWP9GZB9J7u8I4zCmsPqMWXK+MKHYf0sUQS+u4zJwYN2rO4e7sp4VHB4yMJEAfn341DKhXtBct55cOqp1uH7sY/BCScEfy7ppJ9HSLHfGNPo1vfrX4/9XyT8vRhW4IcNT1bGooLDhywK2OXdj+Pee+/l05/+NIODg1x66aWcfvrp3H///Ym/V4mHd0EyYwZcfHFtoZF00s/KXxd2jEm69fn16Ghrs2NPk7T8Ma2G9uNocfTY5EtYDWJ42OYguE01UXt+7N1rcxlGRo681tFhw3nT6HGRxhiDyHrsDoODVtC5BUdnp839aDXBEaUfR1F5HIrSkvglrPnh1+s8ah5J1v66LHNd8vI1esuYdHZm73NqBtRU1eBoP47m5OWXx0dfRZ04s/LXOWQ5uWc9djdp+GNaDRUcDY7242g+hofhiSfGv37GGdEnziwbTmU9uefZLCuJP6YVUcGhKCXDL39hwgQ45ph435dlw6msJ/cimmUp9VHBoSglw88EFNR/uwzo5N56qHNcUUpGWpVwFSUrVONQlBKSp30ftH+FEg3VOHIibj8OgK9+9au8+eabNbfZtm0bp512GosWLeL666+n2fJzmpmgsuFhQ3eTov0rlKio4AgibFPmkGQtOD75yU+yZs0a+vr66Ovr4yc/+UmsfSn5UvSkrf0rlDioqcqPDKqeuftxLFu2jNmzZ/Pd736XAwcO8MEPfpCbbrqJN954gyuuuIKBgQFGR0f5oz/6I1566SV27drFBRdcwKxZs3jwwQfHfffu3bt57bXXOPfccwFYtWoVP/rRj1i+fHmiMSvZkkdJkHpo/wolDio4vGRU9czdj+OBBx7g+9//Po899hjGGFasWMFDDz3E4OAgxx13HD/+8Y8BW/xw+vTp3HrrrTz44IPMmjXL97tffPFF5s2b99b/8+bN48UXX4w9ViUfyjBpa/8KJQ5qqvIStilzAh544AEeeOABzjjjDBYvXsyzzz5LX18fp512Ghs3buSLX/wiDz/8MNOnTw/1fX7+DPHWq1BKh3vSfu01eP55ePXVfCdtjeBS4qAah5e0utDUwBjDjTfeyCc+8Ylx723bto0NGzZw44038v73v58vf/nLdb9v3rx5DAwMvPX/wMAAxx13XGrjVbLBmbRvvRVuv91O2k6+Rp79IPKO4FIaH9U4vGRU9czdj+Oiiy7izjvv5PXXXwesqWnPnj3s2rWLyZMn89GPfpTPfe5zPP744+M+68fcuXOZNm0amzdvxhjD3XffzWWXXZZovEo+TJ4Md9xh1ypDQ9bv0dOTWkxGaPKK4FKaA9U4/Mig6pm7H8fy5cu58sor33JmT506lX/4h39g+/btfP7zn6etrY2Ojg5uu+02AFavXs3y5cuZO3eur3Mc4LbbbuPqq69maGiI5cuXq2O8QdB+EEojov04Whw9NsWi/SCUsqD9OBSlQdB+EEojoqaqBuPss8/mwIEDY1771re+xWmnnVbQiJSkaD8IpdFQwdFgbNmypeghKBmg/SCURkJNVYqiKEokVHAoiqIokVDBoSiKokRCBYeiKIoSCRUcOZF1WfUvfelLnHDCCUydOjXWPhRFUcKigiOAlNtxZC44fv/3f5/HHnss1vcriqJEQcNxfcigHUem/TgAzjnnnGQDVBRFCYkKDg8ZtePItB+HoihKnhRiqhKRD4vIr0TksIgE1kYRkYtF5DkR2S4iN+QxthzacaTej0MpjqB+4YrSzBSlcTwFXA78TdAGItIOfANYBgwAW0VkvTHm6SwHlkM7jtT7cSjF0N9vW7+2tdmGTGefbQsWKkqzU4jGYYx5xhjzXJ3NzgK2G2NeMMYcBL4NZN5kIquic1n241Dyx90vfGTEPm7ZopqH0hqU2cdxPLDT9f8AcHYeO86i6FzW/Ti+8IUvcM899/Dmm28yb948rrnmGv74j/84+cAVX8rQL1xRiiKzfhwishE41uetLxlj7qtuswn4nDGm17uRiHwYuMgYc031/48BZxljPu2z7WpgNcD8+fPftWPHjjHva8+JYPTYxGN4GNavHys42tthxQoVHEpjEqUfR2YahzFmacKvGABOcP0/D9gVsK81wBqwjZwS7ldR6uL0C9+yZayPQ4WG0gqU2VS1FThRRBYCLwIfAa4sdkjFo/04ysOCBTBnjjVPTZmiQkNpHQoRHCLyQeCvgC7gxyLypDHmIhE5DvhbY8wlxphDInIdcD/QDtxpjPlVEeMtE9qPo1xUKiowlNajEMFhjLkXuNfn9V3AJa7/NwAbUtonIpLGVzUNzdZvXlGUfGiJWlWVSoW9e/fqROnCGMPevXup6HJZUZSIlNnHkRrz5s1jYGCAwbQqFjYJlUqFefPmFT0MRVEajJYQHB0dHSxcuLDoYSiKojQFLWGqUhRFUdJDBYeiKIoSCRUciqIoSiQyKzlSFCIyCOyou2ExzAJ+U/QgQqDjTJdGGGcjjBF0nGnjHucCY0yoynxNJzjKjIj0hq0FUyQ6znRphHE2whhBx5k2cceppipFURQlEio4FEVRlEio4MiXNUUPICQ6znRphHE2whhBx5k2scapPg5FURQlEqpxKIqiKJFQwZEhIvJhEfmViBwWkcDIBRG5WESeE5HtInJDnmOs7v8YEflnEemrPs4I2G5URJ6s/q3PcXw1j4+ITBKR71Tf3yIi3XmNLcIYrxaRQdfxu6aAMd4pIntE5KmA90VEvl79Df8mIovzHmN1HPXGeb6I7HMdyy/nPcbqOE4QkQdF5Jnqff4Zn20KPaYhxxj9eBpj9C+jP+AU4CRgE7AkYJt24HngbcBE4BfA23Me51eAG6rPbwBuCdju9QKOYd3jA/whcHv1+UeA75RwjFcDf5338fOM4b3AYuCpgPcvAf4JEOAcYEtJx3k+8P+KPJbVccwFFlefTwP+3ee8F3pMQ44x8vFUjSNDjDHPGGOeq7PZWcB2Y8wLxpiDwLeBy7If3RguA+6qPr8L+EDO+69FmOPjHv/3gQsl3+YrZTiHdTHGPAS8XGOTy4C7jWUzcLSIzM1ndEcIMc5SYIzZbYx5vPp8P/AMcLxns0KPacgxRkYFR/EcD+x0/T9ACic2InOMMbvBXmjA7IDtKiLSKyKbRSQv4RLm+Ly1jTHmELAPmJnL6Dz7rxJ0Dj9UNVd8X0ROyGdokSjDtRiWc0XkFyLyTyLyjqIHUzWPngF4W3SW5pjWGCNEPJ4tUVY9S0RkI3Csz1tfMsbcF+YrfF5LPdSt1jgjfM18Y8wuEXkb8DMR+aUx5vl0RhhImOOTyzGsQZj9/yOwzhhzQESuxWpIv5v5yKJR9HEMy+PY8hivi8glwI+AE4sajIhMBX4AfNYY85r3bZ+P5H5M64wx8vFUwZEQY8zShF8xALhXn/OAXQm/cxy1xikiL4nIXGPM7qoavSfgO3ZVH18QkU3Y1UvWgiPM8XG2GRCRCcB08jV11B2jMWav6987gFtyGFdUcrkWk+Ke+IwxG0TkmyIyyxiTe20oEenATsj/1xjzQ59NCj+m9cYY53iqqap4tgInishCEZmIde7mFrFUZT1wVfX5VcA4TUlEZojIpOrzWcC7gadzGFuY4+Me/x8APzNVr19O1B2jx669AmtrLhvrgVXVSKBzgH2OCbNMiMixjg9LRM7CzmN7a38qk3EIsBZ4xhhza8BmhR7TMGOMdTzz9PC32h/wQeyK4wDwEnB/9fXjgA2u7S7BRjs8jzVx5T3OmcBPgb7q4zHV15cAf1t9fh7wS2zE0C+BnhzHN+74AH8CrKg+rwDfA7YDjwFvK+AY1hvjnwO/qh6/B4GTCxjjOmA3MFK9LnuAa4Frq+8L8I3qb/glAZGAJRjnda5juRk4r6Bxvgdrdvo34Mnq3yVlOqYhxxj5eGrmuKIoihIJNVUpiqIokVDBoSiKokRCBYeiKIoSCRUciqIoSiRUcCiKoiiRUMGhKIqiREIFh6IoihIJFRyKkjEicma1uGFFRKZU+yKcWvS4FCUumgCoKDkgIv8bm+HeCQwYY/684CEpSmxUcChKDlRrWG0FhrElHUYLHpKixEZNVYqSD8cAU7Fd2CoFj0VREqEah6LkgNge7d8GFgJzjTHXFTwkRYmN9uNQlIwRkVXAIWPMPSLSDjwiIr9rjPlZ0WNTlDioxqEoiqJEQn0ciqIoSiRUcCiKoiiRUMGhKIqiREIFh6IoihIJFRyKoihKJFRwKIqiKJFQwaEoiqJEQgWHoiiKEon/DwrWvoMWhixpAAAAAElFTkSuQmCC\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "learning_datasets = []\n", "for dataset in dataset_dfs:\n", @@ -112,11 +247,228 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datagram for 0\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 0.9541740932867725\n", + "\n", + "Datagram for 1\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 0.7767539181383903\n", + "\n", + "Datagram for 0\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 1.1026220160149125\n", + "\n", + "Datagram for 1\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 0.928257284055009\n", + "\n", + "Datagram for 0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 0.7365136224933718\n", + "\n", + "Datagram for 1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 1.0043148676116407\n", + "\n", + "Datagram for 0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 0.8472159320863007\n", + "\n", + "Datagram for 1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 0.9724943016794556\n", + "\n", + "Datagram for 0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 1.0135511778729662\n", + "\n", + "Datagram for 1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean distance for this dataset: 1.0873875476031842\n", + "\n" + ] + } + ], "source": [ "# shows histograms based on distance from first point in dataset\n", "def training_histogram(in_df):\n", @@ -195,9 +547,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[10 1]\n", + " [ 3 9]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.77 0.91 0.83 11\n", + " 1 0.90 0.75 0.82 12\n", + "\n", + " micro avg 0.83 0.83 0.83 23\n", + " macro avg 0.83 0.83 0.83 23\n", + "weighted avg 0.84 0.83 0.83 23\n", + "\n", + "[[7 1]\n", + " [1 9]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.88 0.88 8\n", + " 1 0.90 0.90 0.90 10\n", + "\n", + " micro avg 0.89 0.89 0.89 18\n", + " macro avg 0.89 0.89 0.89 18\n", + "weighted avg 0.89 0.89 0.89 18\n", + "\n", + "[[13 1]\n", + " [ 2 9]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.93 0.90 14\n", + " 1 0.90 0.82 0.86 11\n", + "\n", + " micro avg 0.88 0.88 0.88 25\n", + " macro avg 0.88 0.87 0.88 25\n", + "weighted avg 0.88 0.88 0.88 25\n", + "\n", + "[[11 2]\n", + " [ 0 9]]\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 0.85 0.92 13\n", + " 1 0.82 1.00 0.90 9\n", + "\n", + " micro avg 0.91 0.91 0.91 22\n", + " macro avg 0.91 0.92 0.91 22\n", + "weighted avg 0.93 0.91 0.91 22\n", + "\n", + "[[8 1]\n", + " [4 6]]\n", + " precision recall f1-score support\n", + "\n", + " 0 0.67 0.89 0.76 9\n", + " 1 0.86 0.60 0.71 10\n", + "\n", + " micro avg 0.74 0.74 0.74 19\n", + " macro avg 0.76 0.74 0.73 19\n", + "weighted avg 0.77 0.74 0.73 19\n", + "\n" + ] + } + ], "source": [ "def classifyPoints(test):\n", "\n", @@ -224,7 +638,7 @@ " print(classification_report(y_test, y_pred))\n", " \n", "for dataset in medoid_lst:\n", - " classifyPoints(dataset)" + " classifyPoints(dataset)\n" ] }, { diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 833c5b6..b15bed5 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -25,6 +25,8 @@ \usepackage{csquotes} +\usepackage{todonotes} + %\usepackage[main=english,czech]{babel} % % list of acronyms @@ -101,6 +103,7 @@ \chapter{Introduction} \setsecnumdepth{all} \chapter{Network anomaly detection} +Why do we need network anomaly detection? What are the challenges in anomaly detection? What are the possible methods to tackle these problems? How can machine learning help? What are the challenges of using machine learning? From b5f5cb292f76803f67790ad38668a1c2760e19f0 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 29 Mar 2019 08:42:15 +0100 Subject: [PATCH 17/65] separation into chapters --- text/hlavac-thesis.tex | 245 +++++++++++++++++++++++++++++------------ 1 file changed, 173 insertions(+), 72 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index b15bed5..6e390c3 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -97,90 +97,191 @@ \chapter{Introduction} %them in spaces where only generic similarity measure exists (these spaces do %not necessarily need to be metric). - +\chapter{Goals} +The aim of this work is to study methods used for representation of network host behavioral clusters that can be used in Cognitive targeted anomaly detection framework. +Behavioral similarity used for clustering in this framework does form a metric space, which creates a requirement for methods used. +%TODO incorporate more about what the output should be The output will be an algorithm that can be used in representation of network hosts in Cognitive targeted anomaly detecion framework. +\\ +As result of this thesis should be presented a method, that can be used for representation of given clusters, so that further network hosts added to the system can be correctly added to correct cluster. +Networks protected by this framework can have up to several tens of thousands hosts which leads to huge clusters. +If the classification of newly added hosts would be done by comparing the new host to all others, the memory and time needed for classification would be unmanagable. +That is why memory efficiency is emphasized. +\\ +To reach this major goal general methods used for generic cluster representation in non-metric spaces should be studied. +From these previously studied methods the best ones will be selected and implemented. +As part of this thesis, there should also be created a benchmark dataset for testing the chosen method. +Further goal is to test the chosen methods on general clustering datasets and on the created benchmark dataset. +Based on this testing, find the method that suits best for use in this field and incorporate it into Cognitive targeted anomaly detecion framework. +The selected method will then be finetuned to get the best possible results in anomaly-detection. \setsecnumdepth{all} -\chapter{Network anomaly detection} - -Why do we need network anomaly detection? -What are the challenges in anomaly detection? -What are the possible methods to tackle these problems? -How can machine learning help? What are the challenges of using machine learning? -Introduction to Cognitive targeted anomaly detection. - -\chapter{Representative selection in non-metric spaces} - -Why is it a non-metric space? -What are the algorithms that can be used for representative selection in non-metric spaces? -Problems that need to be tackled using this method. - -\chapter{Comparison of methods} +\chapter{Intrusion Detection System} + +In the following chapter are explained the challenges of network anomaly detection (Section 1), methods used for fast end efficient anomaly detecion (Section 2). +In the last Section 3 the Cognitive targeted anomaly detection system used by Cisco is presented. + +\section{Network Anomaly Detection} +%What is network anomaly detection +Network anomaly detection is a method used in Intrusion Detection Systems (IDS). +IDS is generaly a framework of tools that can are used to detect security threats in a computer network. +There are many well-known IDS widely used in network monigoring infrastructures: +\begin{itemize} + \item Snort + \item Suricata + \item Bro Network Security Monitor + %\item %TODO Get more examples +\end{itemize} +All of these systems are trying to detect, prevent and/or mitigate intrusion attempts or threats. +Attackers that attempt to get into systems usualy want to access information (snooping), manipulate information(man-in-the-middle attacks) or render the system unusable (Denial of Service attacks). +%Network Intrusion Detection System - NIDS +Approaches of detecting thes attacks differ and different methods are more suitable for some attacks. +Here are listed types of IDS as TODO listed them: +\begin{itemize} + \item Signature-based (knowledge-based) + \item Anomaly-based (behavior-based) + \item Stateful protocol analysis (specification based) +\end{itemize} +Cognitive anomaly detection framework that is used in this thesis is an anomaly-based IDS. +\\ \\ +%Network monitoring? +To detect anomalies in a given network, monitoring is set up that collects network flows (using NetFlow protocol). +Based on data collected IDS creates a model of behavior of given network. +This model then serves as a baseline behaviour of given network. +If the behavior suddenly significantly changes this anomaly is detected and reported. +\\ \\ %TODO define network anomaly, DPI +Even though this approach seems very straight forward, there are multiple issues that make it hard to implement. +The environment of computer networks is a dynamic environment that changes based on time, applications run and other factors. +Therefore creating only a static model once and then using it as a baseline without changing does not serve well to detect anomalies. +Modern anomaly-detection systems use a great variety of static and dynamic methods for anomaly detection. +\\ \\ +Much of company infrastructure nowadays rely on network based services. %TODO and digitalization still happens. +Fast detection of security threats is crucial for functioning of most of companies today. +This is why industry leading IDSs are focusing on detecting anomalies in real time, to migitage the loss of resources as soon as possible. +Real-time anomaly detection is done online by monitoring the current traffic and running detection methods on top of them. +If an anomaly is found, system administrator is notified right away and the threat can be anihilated before serious damage happens. +\\ \\ +In real-time network monitoring systems the anomalies are calculated based on time windows of collected traffic. +These time windows are most often set to 5 minutes, it depends on the monitored network. +After collecting traffic for this time window the information gathered is compared to the model saved inside of the system. If the features differ more than certain set threshold it is considered an anomaly. + +\section{Challenges of Real-Time Anomaly Detection} +%Why do we need efficient network anomaly detection? +In theory the best way to find all all anomalies is to be able to save all traffic and calculate the model in previous section continuously on for all hosts. +This way there would be a very specific and precise model for each host in the network. +Approaching it this way, knowing all the information about the network it would be very easy to find anomaly in behaviour of each single host with a 100\% precision. +In reality this is not possible. +Calculating model for each host in even a medium (100 hosts) network is not managable at least not in real time. +In offline anomaly detection system could take two 10GB files of network traffic and calculate different metrics from inspecting each flow, then comparing them and deciding whether a security incident happened. +By the time this long opperation would be completed, the attacker could have done a serious damage in the system. +From the examples above it is clearly seen that for real-time anomaly detection algorithms need to be fast and efficient. +\\ \\ +%What are the challenges in making it fast? +%What are the possible methods to tackle these problems? +Making a model of each single host might not be fast enough. +What if a model is made for the whole network. +Then we have a lot less data to store and calculations are also faster, because the information is aggregated. +This method is widely used and on smaller (several device) networks it is very efficient. +However considering a big network of thousands of devices in so many information we can easily miss one infected device generating different traffic. +The noise in the collected traffic would be too high. +\\ \\ +To give an example imagine detection an infection one printer in a 10 story office building. +This printer has been infected and added to a botnet to generate traffic for DDoS (Distributed Denial of Service) attacks. +Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. +If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personel working in the building. +\\ \\ +%TODO Reference article - network-aware behavior clusterin of internet hosts +A way to approach this problem is explained in [X]. +The model is created by creating bi-partite graphs for comunictaion of hosts in one network prefix. +These graphs are created by looking at hosts from perspective of whom does it communicate with. +From these graphs an adjacency matrix is created. +How adjacent one host is to another is calculated as a normalized weight of edges in the graph. +This adjacency matrix then serves as a baseline model for anomaly detection. +Assessing each time window of traffic means calculating an adjacency matrix for this timewindow and then comparing to the trained model. +If there is a substantional difference an anomaly is detected. +This method is proven to work, however it suffers from the noise of too many hosts explained before. +Also it might not be a good way to look at the traffic from the perspective of a network prefix, as many times a lot of different devices are on one prefix. +\\ \\ +%Other ways +\\ \\ +%false positives +There is also one more problem in creating a good network anomaly detection system - false positive detection. +A false positive is a network anomaly that is detected and is not an actual anomaly. +For example if we would have our anomaly detection system setup wrong it might detect an anomaly everyday at 8AM when workers come to the office, start their computers and download daily email. +Comparing the time window from before 8AM and after 8AM would not give as any relevant information. +This problem can be mitigated by setting window for which the model is calculated long enough, so that these mistakes does not happen. +In other situations false positive alerts can happen and the goal of a good network anomaly detection system is to keep them as close to 0 as possible. + +\section{Cognitive Targeted Anomaly Detection Framework} +%cognitive threat analytics +This section serves as an explanation of Cognitive targeted anomaly detection system for which the methos of this thesis are created. +%TODO explain +\\ \\ +%TODO further reading +This chapter explained basic knowledge needed to unerstand modern approach of network anomaly detection. +For more detailed informatoin a list for further reading is presented. +%TODO list +In the next chapter the theory for representative selection of clusters in non-metric spaces is explained. + +\chapter{Theoretical Overview} +% Theoretical background to clustering and non-metric spaces + +This chapter serves as a theoretical background to explain clustering methods and problematics of non-metric spaces. + +\section{Introduction to Clustering} +% Briefly explain what is Clustering and how is it used in machine learning + +\section{Non-Metric Space} +% jenom pairwise similarity!!! - nemuzeme delat kolecka - neplati geometricka reprezentace okoli +% centroid vubec neni v uvahu - casto nejde vubec udelat +% Louvian Modularity - sestaveni grafu na zaklade pairwise similarit - to co se pouziva v Threat - modularity clustering, community detection + +\chapter{Representative Selection} +% Representative selection + +\section{Approaches to Representative Selection} +% popsani jak by se to delalo normalne +% stezejni myslenky proc to delame takto + +%What are the algorithms that can be used for representative selection in non-metric spaces? +%Problems that need to be tackled using this method. + +\chapter{Implementation and Performance Testing of Relevant Methods} + +\section{Methods Relevant to Cognitive Anomaly Detection} +The following methods were tested and compared as a part of this thesis. +\begin{itemize} + \item Random selection + \item Greedy Selection + \item Delta-Medoids One-Shot + \item Delta-Medoids %TODO citations +\end{itemize} + +\subsection{Greedy Selection} %explanation of my method + +\subsection{Random Selection} + +\subsection{Delta-Medoids One-Shot} + +\subsection{Delta-Medoids} -Testbed explanation - why is it the best etc. -What datasets am I using and why? What criteria were chosen to compare algoritms? Algorithms comparison. Summary on what algorithm was chosen and why is it the best one. -\chapter{Testing the best method in reality} +\section{Datasets} +The task of cluster representation in non-metric spaces generally is not necessarily connected only to the Security field. +That is why the selected algorithms were tested on both generally know datasets like pendigits and multiple features and on specific data collected from network. +What datasets am I using and why? + +\section{Testbed} + +\chapter{Results} +% Testbed explanation - why is it the best etc. What were the parameters when this method has been used? How did it perform compared to random selection? -%% good to make a small paragraph what is this chapter about good for readability -%V této kapitole provedeme analýzu požadavků a návrh vÄetnÄ› zdůvodnÄ›ní vÅ¡ech -%rozhodnutí. -% -%Also we could write what sections do we have here. -% -%\section[Requirememnts]{Requirement analysis} -% -%Text -- zejména ten odborný -- je nutné Älenit na odstavce. Každý odstavec by se -%mÄ›l týkat jednoho tématu, myÅ¡lenky\dots{} Odstavce od sebe musí být vizuálnÄ› -%oddÄ›lené. K tomu existuje nÄ›kolik vhodných stylů, které si popíšeme v jedné z -%následujících kapitol. Odstavce mohou být různÄ› vysázené. V odborných textech je -%běžná sazba ``do bloku''. PÅ™i ní je nutné vhodnÄ› mÄ›nit mezislovní mezery. Jejich -%doporuÄená velikost je 0,25--0,33~ÄtverÄíku. \enquote{something quoted} \\ -%% ~ -> nezalomitelna mezera -% -% -%\noindent -%Požadavky jsou tÄ›chto typů: -%\begin{description} -% \item[funkÄní] objasňují, co se musí udÄ›lat a identifikují nutné aktivity; -% \item[nefunkÄní] jsou vÅ¡echny, které nejsou funkÄní. Typicky mezi nÄ› patří: -% \begin{itemize} -% \item výkonnostní, -% \item designové. -% \end{itemize} -%\end{description} -% -%\subsection{FunkÄní požadavky} -% -%FunkÄní požadavky této práce jsou: -%\begin{enumerate} -% \item získat zadání, -% \item sepsat práci, -% \item vÄas odevzdat, -% \item obhájit\footnote{Když se zadaří.}. -%\end{enumerate} -% -%\section{Tabulky} -% -%V tabulce \ref{tab:body} najdete možnosti, jak získat body v pÅ™edmÄ›tu BI-DPR. -% -%\begin{table}[th] \centering -%\caption{Bodované Äinnosti pÅ™edmÄ›tu BI-DPR} \label{tab:body} -% \begin{tabular}{|l|r|c|} -% \hline -% Äinnost & body & povinná \\ \hline \hline -% test citace & 10 & ne \\ \hline -% test typografie & 10 & ne \\ \hline -% hodnocení vedoucího & 20 & ne \\ \hline -% ohbahoba poziÄní zprávy & 60 & ano \\ \hline -% \end{tabular} -%\end{table} \setsecnumdepth{part} \chapter{Conclusion} From b05fc2d8e48fa211e5edfc1add63e8963cb04b25 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 5 Apr 2019 09:46:42 +0200 Subject: [PATCH 18/65] added first chapter to text --- text/FITthesis.cls | 2 +- text/hlavac-thesis.bib | 33 + text/hlavac-thesis.tex | 233 +++--- text/iso690.bst | 1604 ++++++++++++++++++++++++++++++++++++++++ 4 files changed, 1772 insertions(+), 100 deletions(-) create mode 100644 text/hlavac-thesis.bib create mode 100644 text/iso690.bst diff --git a/text/FITthesis.cls b/text/FITthesis.cls index 50d7ca3..d68d5e6 100644 --- a/text/FITthesis.cls +++ b/text/FITthesis.cls @@ -17,7 +17,7 @@ } \DeclareOption{english}{ \AtBeginDocument{\selectlanguage{english}} - \PassOptionsToPackage{english}{babel} + \PassOptionsToPackage{main=english, czech}{babel} \def\@lang{0} } \DeclareOption{slovak}{ diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib new file mode 100644 index 0000000..454a405 --- /dev/null +++ b/text/hlavac-thesis.bib @@ -0,0 +1,33 @@ +@article{mukherjee1994network, + title={Network intrusion detection}, + author={Mukherjee, Biswanath and Heberlein, L Todd and Levitt, Karl N}, + journal={IEEE network}, + volume={8}, + number={3}, + pages={26--41}, + year={1994}, + publisher={IEEE} +} +@inproceedings{xu2011network, + title={Network-aware behavior clustering of Internet end hosts}, + author={Xu, Kuai and Wang, Feng and Gu, Lin}, + booktitle={2011 Proceedings IEEE INFOCOM}, + pages={2078--2086}, + year={2011}, + organization={IEEE} +} +@inproceedings{kopp2018community, + title={Community-based anomaly detection}, + author={Kopp, Martin and Grill, Martin and Kohout, Jan}, + booktitle={2018 IEEE International Workshop on Information Forensics and Security (WIFS)}, + pages={1--6}, + year={2018}, + organization={IEEE} +} +@online{hron2018breaches, + author={Hron, Martin}, + title={The 10 Biggest Data Breaches in 2018}, + year={2018}, + url={https://blog.avast.com/biggest-data-breaches}, + urldate={2019-04-05} +} diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 6e390c3..0436df0 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -63,26 +63,29 @@ \setsecnumdepth{part} \chapter{Introduction} -While network communication over the Internet is very efficient and helps everyday life of many people around the world, there is a lot of security issues concerning the shared medium infrastructure of the Internet. -Networks are becoming very complex as new services are being deployed. -Every day security engineers are trying to prevent different kind of threats - ranging from spam flooding user inboxes to bruteforcing specific devices on the edge of internal networks. -Once it might have been enough to set up a firewall and change default usernames and passwords. -Now there is need for much more sophisticated defense mechanisms. -Even though multi-layered defense systems are deployed on networks, there is still is many exploits in software and hardware that attackers can use to breach the defense mechanisms and hack themselves into a network. -Once they are inside a network it often takes a long time to notice that something is amiss. -This problem can tackled by comparing the networks current behavior compared to how it is expected to behave. -Usualy a border device in that given network is gathering information about behavior and if it detects that the network behaves differently than expected security personel are notified about possible anomaly. \\ - -In big networks of 1000 network hosts and more small anomalies can be difficult to find by the method explained above. -For example single infected printer that is used in DDoS botnet can be easily missed in the traffic of a 3000 employee company. -This is the scenario that is seen very often lately and few different methods were designed to find these kinds of anomalies. \\ - -The method I focus on in this work is used in Cognitive targeted anomaly detection framework and is based on clustering the network hosts based on their behavior and each cluster is monitored separately. -In each cluster there are devices that are behaving in similar manner, therefore any anomalous behavior for such device is easier to detect. -The problem that arises with this method is how to represent these clusters. -In each cluster there can be up to several thousand of hosts which is not convenient while trying to detect anomalies in real time. -I modified, implemented and tested 4 algorithms for cluster representation in non-metric space as the similarity measure used does not form a metric space. - +Even after several decades after the Internet became the most used medium for communication, there are still many issues concerning its security. Businesses all around the world daily connect more of their infrastructure to the Internet thus creating a possibility for attackers to exploit the connected devices for their malicious use. Security engineers are always inventing new methods to stop the attacks and secure their networks. Attackers race to overcome these newly created methods and to gain control of protected data or devices. This race creates a need for much more sophisticated defense mechanisms. \\ + +Multi-layer defense systems are protecting contemporary networks. As attackers overcome one layer, there is another layer waiting for them after it. However, if the succeed to bypass all of them unnoticed a colossal problem can occur. They can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. It can take a long time before the breach is noticed. A similar thing happened in Marriot International child company Starwood last year. Names, emails, addresses and credit card numbers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014\cite{hron2018breaches}. \\ + +One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). NBAD compares the current behavior of the network to a model created from previous behavior. Devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. Models are calculated from the data collected. If a significant change in behavior is detected, security personnel are notified, and they can act accordingly. \\ + +It is normal for a company to have up to millions of devices. Straight forward usage of NBAD in these big networks is not sufficient to detect breaches successfully. For example, a single infected device that can be easily missed in the traffic of a bigger company. In order to detect such anomalies in this network, it is needed to focus on smaller parts of it. Clustering based on the behavior of network hosts can be used to separate the network into smaller pieces. \\ + +Cognitive targeted anomaly detection framework from Cisco serves as an example of this clustering. Each cluster that it creates consists of hosts that behave similarly. The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ + +Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ + +(Chapters and sections will be briefly explained here.) + +%The main focus of this work is in representing these clusters found by clustering algorithms. +%Finding representation of clusters in Cognitive targeted anomaly detection framework is made more difficult by the fact, that similarity measure used for clustering does not form a metric space. +%Therefore the focus on methods that can be used for selecting representatives in non-metric spaces. \\ +% +%Each cluster can have up to several thousands of hosts. +%Algorithms considered in this thesis were selected based on the following expectations. +%As the hosts are clustered based on their behavior, it is safe to expect that selecting a subset of the cluster can represent its behavior without losing significant information. +%Furthermore having fewer hosts as representatives of each cluster significantly reduce memory usage. \\ + %Why use clustering methods in network anomaly detection? %Why do we need representative selection? %What are the next sections about? @@ -98,100 +101,101 @@ \chapter{Introduction} %not necessarily need to be metric). \chapter{Goals} -The aim of this work is to study methods used for representation of network host behavioral clusters that can be used in Cognitive targeted anomaly detection framework. -Behavioral similarity used for clustering in this framework does form a metric space, which creates a requirement for methods used. -%TODO incorporate more about what the output should be The output will be an algorithm that can be used in representation of network hosts in Cognitive targeted anomaly detecion framework. -\\ -As result of this thesis should be presented a method, that can be used for representation of given clusters, so that further network hosts added to the system can be correctly added to correct cluster. -Networks protected by this framework can have up to several tens of thousands hosts which leads to huge clusters. -If the classification of newly added hosts would be done by comparing the new host to all others, the memory and time needed for classification would be unmanagable. -That is why memory efficiency is emphasized. -\\ -To reach this major goal general methods used for generic cluster representation in non-metric spaces should be studied. -From these previously studied methods the best ones will be selected and implemented. -As part of this thesis, there should also be created a benchmark dataset for testing the chosen method. -Further goal is to test the chosen methods on general clustering datasets and on the created benchmark dataset. -Based on this testing, find the method that suits best for use in this field and incorporate it into Cognitive targeted anomaly detecion framework. -The selected method will then be finetuned to get the best possible results in anomaly-detection. +This work aims to study methods used for the representation of network host behavioral clusters that can be used in Cognitive targeted anomaly detection framework. +Behavioral similarity used for clustering in this framework does form a metric space, which creates a requirement for algorithms used for representative selection. \\ + +As a result of this thesis should be presented a method, that can be used for the representation of given clusters so that further network hosts added to the system can be correctly added to the correct cluster. +Networks monitored by this framework can have up to several tens of thousands of hosts which leads to massive clusters. +If the classification of newly added hosts were done by comparing the new host to all others, the memory and time needed for classification would be unmanageable. +That is why memory efficiency is emphasized. \\ + +To reach this goal general methods used for general cluster representation in non-metric spaces should be studied. +From these previously studied methods, the best ones will be selected and implemented. +As part of this thesis, there should also be created a benchmark dataset for testing the chosen method. +A further goal is to test the chosen methods on generally used clustering datasets and the created benchmark dataset. +Based on this testing, find the method that suits best for use in this field and incorporates it into the Cognitive targeted anomaly detection framework. +The selected method should be finetuned to get the best possible results in anomaly-detection. \setsecnumdepth{all} + +%========================================CHAPTER================================== + \chapter{Intrusion Detection System} -In the following chapter are explained the challenges of network anomaly detection (Section 1), methods used for fast end efficient anomaly detecion (Section 2). -In the last Section 3 the Cognitive targeted anomaly detection system used by Cisco is presented. +The following chapter focuses on presenting ideas from network security needed to understand the main focus of this thesis. +Different approaches of network intrusion detection systems (IDS) are explained in Section 1. +In Section 2 the scope is narrowed to network anomaly detection IDS. +Section 3 serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. +Methods researched in this thesis were tested on real-life data collected from this framework. \\ + +\section{Types of Intrusion Detection Systems} -\section{Network Anomaly Detection} -%What is network anomaly detection -Network anomaly detection is a method used in Intrusion Detection Systems (IDS). -IDS is generaly a framework of tools that can are used to detect security threats in a computer network. -There are many well-known IDS widely used in network monigoring infrastructures: +An intrusion detection system (IDS) is a security tool designed for identification of unauthorized use or abuse of computer systems by both system insiders and external penetrators\cite{mukherjee1994network}. +IDS that is designed specifically for monitoring computer network is called Network IDS (NIDS). +Few of commonly used NIDS software is listed below: \begin{itemize} \item Snort \item Suricata \item Bro Network Security Monitor %\item %TODO Get more examples \end{itemize} -All of these systems are trying to detect, prevent and/or mitigate intrusion attempts or threats. -Attackers that attempt to get into systems usualy want to access information (snooping), manipulate information(man-in-the-middle attacks) or render the system unusable (Denial of Service attacks). -%Network Intrusion Detection System - NIDS -Approaches of detecting thes attacks differ and different methods are more suitable for some attacks. -Here are listed types of IDS as TODO listed them: +These systems are used on computer networks to detect and sometimes even prevent attacks that are threats to one of the essential services of such a network. +These basic services are\cite{mukherjee1994network}: \begin{itemize} - \item Signature-based (knowledge-based) - \item Anomaly-based (behavior-based) - \item Stateful protocol analysis (specification based) + \item Data confidentiality + \item Data and communication integrity + \item Accessibility \end{itemize} -Cognitive anomaly detection framework that is used in this thesis is an anomaly-based IDS. -\\ \\ -%Network monitoring? -To detect anomalies in a given network, monitoring is set up that collects network flows (using NetFlow protocol). -Based on data collected IDS creates a model of behavior of given network. -This model then serves as a baseline behaviour of given network. -If the behavior suddenly significantly changes this anomaly is detected and reported. -\\ \\ %TODO define network anomaly, DPI -Even though this approach seems very straight forward, there are multiple issues that make it hard to implement. -The environment of computer networks is a dynamic environment that changes based on time, applications run and other factors. -Therefore creating only a static model once and then using it as a baseline without changing does not serve well to detect anomalies. -Modern anomaly-detection systems use a great variety of static and dynamic methods for anomaly detection. -\\ \\ -Much of company infrastructure nowadays rely on network based services. %TODO and digitalization still happens. -Fast detection of security threats is crucial for functioning of most of companies today. -This is why industry leading IDSs are focusing on detecting anomalies in real time, to migitage the loss of resources as soon as possible. -Real-time anomaly detection is done online by monitoring the current traffic and running detection methods on top of them. -If an anomaly is found, system administrator is notified right away and the threat can be anihilated before serious damage happens. -\\ \\ -In real-time network monitoring systems the anomalies are calculated based on time windows of collected traffic. -These time windows are most often set to 5 minutes, it depends on the monitored network. -After collecting traffic for this time window the information gathered is compared to the model saved inside of the system. If the features differ more than certain set threshold it is considered an anomaly. +Attackers, on the other hand, try to disrupt these services by accessing confidential information (snooping), manipulating information (man-in-the-middle attacks) or disabling access to network services (Denial of Service attacks). +IDS must have multiple components to detect as many of these attacks as is possible. +As each attack kind of intrusion is better detected by a different method, there are several types of IDS: +\begin{itemize} + \item Signature-based (knowledge-based) %TODO put it into sentences with an explanation + \item Anomaly-based (behavior-based) + \item Stateful protocol analysis (specification based) +\end{itemize} %TODO citation +Anomaly-based IDS serves as a foundation for work presented in this thesis. + +\section{Network Anomaly Detection} +Network anomaly detection is a method used in Intrusion Detection Systems (IDS) as explained in previous section. +This method focuses on comparing network host behavior changes in time. +If a change greater than a certain threshold is observed, network anomaly is detected. +In IDS when an anomaly is detected, an alert is created to inform network administrator about what has happened. +This alert can be any kind of message ranging from a syslog message to an email sent to the admin. \\ + +Host behavior is collected from devices in the network. +There is at least one (although many times multiple) device set up for collecting network flows (using NetFlow protocol). +Flow collection is the most widely used method for gathering data on the network. %TODO citation +One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and number of data in each direction. % [RFC2722] TODO citation +This aggregation of information allows much faster (even real-time) detection of problems on a network as compared to for example deep packet inspection (DPI). +DPI is another method used to detect problems in a network. +It focuses on exploring the payload of each packet. +In today's networks, this method is quickly becoming obsolete as the majority of traffic is encrypted, and therefore it is hard to perform DPI. \\ + +Network flows are sent via NetFlow protocol to one place where they are stored, and detection mechanisms are performed based on the data collected. +Network anomaly detection system creates a model of the behavior of the given network, which serves as a baseline behavior of a given network for a specific time window. +Further traffic is then collected and another model of behavior for another time window is computed. +If the behavior suddenly significantly changes it is considered to be an anomaly that is reported by the IDS. \\ %TODO talk better about time windows \section{Challenges of Real-Time Anomaly Detection} -%Why do we need efficient network anomaly detection? In theory the best way to find all all anomalies is to be able to save all traffic and calculate the model in previous section continuously on for all hosts. This way there would be a very specific and precise model for each host in the network. Approaching it this way, knowing all the information about the network it would be very easy to find anomaly in behaviour of each single host with a 100\% precision. -In reality this is not possible. -Calculating model for each host in even a medium (100 hosts) network is not managable at least not in real time. -In offline anomaly detection system could take two 10GB files of network traffic and calculate different metrics from inspecting each flow, then comparing them and deciding whether a security incident happened. -By the time this long opperation would be completed, the attacker could have done a serious damage in the system. -From the examples above it is clearly seen that for real-time anomaly detection algorithms need to be fast and efficient. -\\ \\ -%What are the challenges in making it fast? -%What are the possible methods to tackle these problems? -Making a model of each single host might not be fast enough. -What if a model is made for the whole network. -Then we have a lot less data to store and calculations are also faster, because the information is aggregated. +In reality this is not possible because hosts do not generate enough traffic to create a good model of their behavior.a \\ + +That is why models are calculated for whole networks where it is possible to create a model that reflects the behavior of the network. This method is widely used and on smaller (several device) networks it is very efficient. However considering a big network of thousands of devices in so many information we can easily miss one infected device generating different traffic. -The noise in the collected traffic would be too high. -\\ \\ +The noise in the collected traffic would be too high. \\ + To give an example imagine detection an infection one printer in a 10 story office building. This printer has been infected and added to a botnet to generate traffic for DDoS (Distributed Denial of Service) attacks. Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. -If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personel working in the building. -\\ \\ +If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personel working in the building. \\ + %TODO Reference article - network-aware behavior clusterin of internet hosts -A way to approach this problem is explained in [X]. +A way to approach this problem is explained in \cite{xu2011network}. The model is created by creating bi-partite graphs for comunictaion of hosts in one network prefix. These graphs are created by looking at hosts from perspective of whom does it communicate with. From these graphs an adjacency matrix is created. @@ -213,15 +217,46 @@ \section{Challenges of Real-Time Anomaly Detection} In other situations false positive alerts can happen and the goal of a good network anomaly detection system is to keep them as close to 0 as possible. \section{Cognitive Targeted Anomaly Detection Framework} -%cognitive threat analytics -This section serves as an explanation of Cognitive targeted anomaly detection system for which the methos of this thesis are created. -%TODO explain -\\ \\ -%TODO further reading -This chapter explained basic knowledge needed to unerstand modern approach of network anomaly detection. -For more detailed informatoin a list for further reading is presented. -%TODO list -In the next chapter the theory for representative selection of clusters in non-metric spaces is explained. + +% Cognitive Threat Analytics +Algorithms studied in this thesis are tested as a part of Cognitive Targeted Anomaly Detection Framework which is a part of Cognitive Threat Analytics developed and used by Cisco. +This framework successfuly uses community-based clustering on behaviors of each host to separate whole network into smaller groups. +Running anomaly detection for each group then yields significantly better results as compared to traditional whole network approach. +Not only it works better because of commynity-based clustering, but also because of the ability to adapt dynamically as the network changes. +Thus it is able incorporate changes that happen on given network such as adding and removing devices.\\ + +To dive into more detail the method is separated into 2 phases. +Initial phase is used to learn the first state of the network, second ongoing phase starts when initial model is created. +During this ongoing phase the framework is able to dynamically adjust clusters to the current state of the network. \\ + +Initial phase starts by collecting 24 hours of traffic from given network. +Data collected consists of network traffic flows and proxy server logs. +Once the 24-hour period is over, clustering of hosts starts. +Each host is represented by a tuple $h = {S^h, F^h}$, where: +\begin{itemize} + \item $S^h$ represents set of all visited pairs server:port + \item $F^h$ represents the frequency of visits of server:port pairs +\end{itemize} +This frequency-based approach is chosen so that some servers like Facebook or Google do not overwhelm the statistics in the model. +When each host has its representation the community-based clustering algorithm is started using similarity measure of: +$$cos(h, h') = \frac{\sum_{s \in S} F_s^h F_s^{h'}} {\sqrt{\sum_{s \in S} (F_s^h)^2} \sqrt{\sum_{s \in S} (F_s^{h'})^2}}$$ +%TODO read about and explain community based clustering +For clusters that are bigger than 30 hosts representatives are selected. %TODO check if it is really 30 +Each host is then given a random number between 1 and 12 which serves as time to live for that method. %TODO check the range +This time to live is crucial for dynamic changes in the network, because of it clusters can change or even die if they do not maintain their behavior. +Currently 30 hosts are selected at random for each cluster. +In this thesis tests were made to improve this random selection method. + +After the initial training phase is over and the model is established, data continues to be collected. +Every 4 hours another all hosts that are observed are assigned to existing clusters using community-based clustering explained above. +For eaach cluster number serving as time to live is decreased and hosts that reach 0 are removed from the cluster. +Free spaces are filled with hosts selected form hosts that were assigned to the cluster from this 4 hour period. +This process goes on and every 4 hours clusters are updated. + +These clusters are then further used for in anomaly detection. +They serve as a foundation for calculating baseline behavior for hosts belonging to it. + +%========================================CHAPTER================================== \chapter{Theoretical Overview} % Theoretical background to clustering and non-metric spaces @@ -289,7 +324,7 @@ \chapter{Conclusion} Which one is the best and under what circumstances? \bibliographystyle{iso690} -\bibliography{mybibliographyfile} +\bibliography{hlavac-thesis.bib} \setsecnumdepth{all} \appendix diff --git a/text/iso690.bst b/text/iso690.bst new file mode 100644 index 0000000..1932ae2 --- /dev/null +++ b/text/iso690.bst @@ -0,0 +1,1604 @@ +%% +%% This is file `czech.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `head,exlang,lang,vonx,nm-rev,jnrlst,aunm-semi,nmlm,x3,m3,atit-u,vnum-nr,volp-sp,jwdpg,pp-last,jwdvol,num-xser,ser-vol,ser-ed,pg-bk,add-pub,isbn,issn,doi,edpar,edby,blk-tit,au-col,èe¹tina,pp,and-xcom,xand,eprint,biburl,url-nl,nfss,,{}') +%% czech.mbs (with options: `exlang,lang,vonx,nm-rev,jnrlst,aunm-semi,nmlm,x3,m3,atit-u,vnum-nr,volp-sp,jwdpg,pp-last,jwdvol,num-xser,ser-vol,ser-ed,pg-bk,add-pub,isbn,issn,doi,edpar,edby,blk-tit,au-col,èe¹tina,pp,and-xcom,xand,eprint,biburl,url-nl,nfss,,{}') +%% merlin.mbs (with options: `tail,exlang,lang,vonx,nm-rev,jnrlst,aunm-semi,nmlm,x3,m3,atit-u,vnum-nr,volp-sp,jwdpg,pp-last,jwdvol,num-xser,ser-vol,ser-ed,pg-bk,add-pub,isbn,issn,doi,edpar,edby,blk-tit,au-col,èe¹tina,pp,and-xcom,xand,eprint,biburl,url-nl,nfss,,{}') +%% ---------------------------------------- +%% *** Czech style CSN ISO 690 *** +%% +%% Copyright 1994-2004 Patrick W Daly +%% Copyright 2011 David Madle + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above. + % + % This generated file can be redistributed and/or modified under the terms + % of the LaTeX Project Public License Distributed from CTAN + % archives in directory macros/latex/base/lppl.txt; either + % version 1 of the License, or any later version. + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2004/02/09 4.13 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in + % This is a numerical citation style, and as such is standard LaTeX. + % It requires no extra package to interface to the main text. + % The form of the \bibitem entries is + % \bibitem{key}... + % Usage of \cite is as follows: + % \cite{key} ==>> [#] + % \cite[chap. 2]{key} ==>> [#, chap. 2] + % where # is a number determined by the ordering in the reference list. + % The order in the reference list is alphabetical by authors. + %--------------------------------------------------------------------- + +ENTRY + { address + archive + author + booktitle + chapter + doi + edition + editor + eid + eprint + howpublished + institution + isbn + issn + journal + key + language + month + note + number + organization + pages + publisher + school + series + title + type + url + volume + year + } + {} + { label } +INTEGERS { output.state before.all mid.sentence after.sentence after.block } +FUNCTION {init.state.consts} +{ #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +FUNCTION {output.nonnull} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry} +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {add.colon} +{ duplicate$ empty$ + 'skip$ + { ":" * add.blank } + if$ +} + +FUNCTION {date.block} +{ + skip$ +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {new.block.checka} +{ empty$ + 'skip$ + 'new.block + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {new.sentence.checka} +{ empty$ + 'skip$ + 'new.sentence + if$ +} +FUNCTION {new.sentence.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.sentence + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + %------------------------------------------------------------------- + % Begin module: + % \ProvidesFile{czech.mbs}[2011/07/09 1.0 (DM)] + + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et al." } + +FUNCTION {bbl.editors} +{ "editors" } + +FUNCTION {bbl.editor} +{ "editor" } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edition" } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pp." } + +FUNCTION {bbl.page} +{ "p." } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Dissertation thesis" } + +FUNCTION {bbl.first} +{ "First" } + +FUNCTION {bbl.second} +{ "Second" } + +FUNCTION {bbl.third} +{ "Third" } + +FUNCTION {bbl.fourth} +{ "Fourth" } + +FUNCTION {bbl.fifth} +{ "Fifth" } + +FUNCTION {bbl.st} +{ "st" } + +FUNCTION {bbl.nd} +{ "nd" } + +FUNCTION {bbl.rd} +{ "rd" } + +FUNCTION {bbl.th} +{ "th" } + +MACRO {jan} {"Jan."} + +MACRO {feb} {"Feb."} + +MACRO {mar} {"Mar."} + +MACRO {apr} {"Apr."} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"Aug."} + +MACRO {sep} {"Sept."} + +MACRO {oct} {"Oct."} + +MACRO {nov} {"Nov."} + +MACRO {dec} {"Dec."} + + % End module: czech.mbs +%% Copyright 1994-2004 Patrick W Daly +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {format.eprint} +{ eprint duplicate$ empty$ + 'skip$ + { "\eprint" + archive empty$ + 'skip$ + { "[" * archive * "]" * } + if$ + "{" * swap$ * "}" * + } + if$ +} +FUNCTION {write.url} +{ url empty$ + { skip$ } + { "\urlprefix\biburl{" url * "}" * write$ newline$ } + if$ +} + +STRINGS { bibinfo} +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}{, f.}{, jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + nameptr #2 + #1 + = + numnames #2 + > and + { "others" 't := + #1 'namesleft := } + 'skip$ + if$ + namesleft #1 > + { "; " * t * } + { + ";" * + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { " " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{f.~}{vv~}{ll}{, jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { "; " * t * } + { + ";" * + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + + " " * bbl.etal * + } + { " " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.authors} +{ author "author" format.names +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + " " * + get.bbl.editor + "(" swap$ * ")" * + * + } + if$ +} +FUNCTION {format.book.pages} +{ pages "pages" bibinfo.check + duplicate$ empty$ 'skip$ + { " " * bbl.pages * } + if$ +} +FUNCTION {format.isbn} +{ isbn "isbn" bibinfo.check + duplicate$ empty$ 'skip$ + { + "ISBN " swap$ * + } + if$ +} + +FUNCTION {format.issn} +{ issn "issn" bibinfo.check + duplicate$ empty$ 'skip$ + { + "ISSN " swap$ * + } + if$ +} + +FUNCTION {format.doi} +{ doi "doi" bibinfo.check + duplicate$ empty$ 'skip$ + { + "\doi{" swap$ * "}" * + } + if$ +} +FUNCTION {select.language} +{ duplicate$ empty$ + 'pop$ + { language empty$ + 'skip$ + { "{\selectlanguage{" language * "}" * swap$ * "}" * } + if$ + } + if$ +} + +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + "title" bibinfo.check + duplicate$ empty$ 'skip$ + { +% select.language + } + if$ +} +FUNCTION {output.bibitem} +{ newline$ + "\bibitem{" write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + " " * } + +FUNCTION {format.date} +{ + month "month" bibinfo.check + duplicate$ empty$ + year "year" bibinfo.check duplicate$ empty$ + { swap$ 'skip$ + { "there's a month but no year in " cite$ * warning$ } + if$ + * + } + { swap$ 'skip$ + { + swap$ + " " * swap$ + } + if$ + * + } + if$ +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + emphasize +% select.language + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { emphasize ", " * swap$ * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} +FUNCTION {is.num} +{ chr.to.int$ + duplicate$ "0" chr.to.int$ < not + swap$ "9" chr.to.int$ > not and +} + +FUNCTION {extract.num} +{ duplicate$ 't := + "" 's := + { t empty$ not } + { t #1 #1 substring$ + t #2 global.max$ substring$ 't := + duplicate$ is.num + { s swap$ * 's := } + { pop$ "" 't := } + if$ + } + while$ + s empty$ + 'skip$ + { pop$ s } + if$ +} + +FUNCTION {convert.edition} +{ extract.num "l" change.case$ 's := + s "first" = s "1" = or + { bbl.first 't := } + { s "second" = s "2" = or + { bbl.second 't := } + { s "third" = s "3" = or + { bbl.third 't := } + { s "fourth" = s "4" = or + { bbl.fourth 't := } + { s "fifth" = s "5" = or + { bbl.fifth 't := } + { s #1 #1 substring$ is.num + { s bbl.th * 't := } + { edition 't := } + if$ + } + if$ + } + if$ + } + if$ + } + if$ + } + if$ + t +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + convert.edition + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ": " * + swap$ + n.dashify + pages multi.page.check + 'bbl.pages + 'bbl.page + if$ + swap$ tie.or.space.prefix + "pages" bibinfo.check + * * + * + } + if$ + } + if$ +} +FUNCTION {format.journal.eid} +{ eid "eid" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ 'skip$ + { + ": " * + } + if$ + swap$ * + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + bbl.volume swap$ tie.or.space.prefix + "volume" bibinfo.check + * * + } + if$ + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + ", " bbl.nr * number tie.or.space.prefix pop$ * swap$ * + } + if$ * +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + { "" } + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check + emphasize +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + format.bvolume duplicate$ empty$ 'pop$ + { ", " swap$ * * } + if$ + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + bbl.edby + " " * swap$ * + swap$ + "," * + " " * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {empty.misc.check} +{ author empty$ title empty$ howpublished empty$ + month empty$ year empty$ note empty$ + and and and and and + key empty$ not and + { "all relevant fields are empty in " cite$ * warning$ } + 'skip$ + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + key duplicate$ empty$ + { pop$ + journal duplicate$ empty$ + { "need key or journal for " cite$ * " to crossref " * crossref * warning$ } + { "journal" bibinfo.check emphasize word.in swap$ * } + if$ + } + { word.in swap$ * " " *} + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.crossref.editor} +{ editor #1 "{vv~}{ll}" format.name$ + "editor" bibinfo.check + editor num.names$ duplicate$ + #2 > + { pop$ + "editor" bibinfo.check + " " * bbl.etal + * + } + { #2 < + 'skip$ + { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + "editor" bibinfo.check + " " * bbl.etal + * + } + { + bbl.and space.word + * editor #2 "{vv~}{ll}" format.name$ + "editor" bibinfo.check + * + } + if$ + } + if$ + } + if$ +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { series empty$ + { "need editor, key, or series for " cite$ * " to crossref " * + crossref * warning$ + "" * + } + { series emphasize * } + if$ + } + { key * } + if$ + } + { format.crossref.editor * } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { format.booktitle duplicate$ empty$ + { "need editor, key, or booktitle for " cite$ * " to crossref " * + crossref * warning$ + } + { word.in swap$ * } + if$ + } + { word.in key * " " *} + if$ + } + { word.in format.crossref.editor * " " *} + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + address "address" bibinfo.check * + t empty$ + 'skip$ + { address empty$ + 'skip$ + { ": " * } + if$ + t * + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + new.sentence + format.title "title" output.check + new.sentence + crossref missing$ + { + journal + "journal" bibinfo.check + emphasize + "journal" output.check + format.vol.num.pages output + format.date "year" output.check + } + { format.article.crossref output.nonnull + } + if$ + eid empty$ + { format.journal.pages } + { format.journal.eid } + if$ + format.issn output + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + new.sentence + } + { format.authors output.nonnull + new.sentence + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + new.sentence + format.number.series output + format.publisher.address output + } + { + new.sentence + format.book.crossref output.nonnull + } + if$ + format.edition output + format.date "year" output.check + format.isbn output + format.book.pages output + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + new.sentence + format.title "title" output.check + new.sentence + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + format.date output + format.isbn output + format.book.pages output + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + new.sentence + } + { format.authors output.nonnull + new.sentence + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.btitle "title" output.check + crossref missing$ + { + format.bvolume output + format.chapter.pages "chapter and pages" output.check + new.sentence + format.number.series output + format.publisher.address output + } + { + format.chapter.pages "chapter and pages" output.check + new.sentence + format.book.crossref output.nonnull + } + if$ + format.edition output + format.date "year" output.check + crossref missing$ + { format.isbn output } + 'skip$ + if$ + format.pages "pages" output.check + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + new.sentence + format.title "title" output.check + new.sentence + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.number.series output + format.chapter.pages output + format.publisher.address output + format.edition output + format.date "year" output.check + format.isbn output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + format.pages "pages" output.check + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + new.sentence + format.title "title" output.check + new.sentence + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.number.series output + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + format.publisher.address output + } + if$ + format.date "year" output.check + format.isbn output + format.issn output + } + { format.incoll.inproc.crossref output.nonnull + } + if$ + format.pages "pages" output.check + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + author empty$ + { organization "organization" bibinfo.check + duplicate$ empty$ 'pop$ + { output + address "address" bibinfo.check output + } + if$ + } + { format.authors output.nonnull } + if$ + new.sentence + format.btitle "title" output.check + new.sentence + author empty$ + { organization empty$ + { + address "address" bibinfo.check output + } + 'skip$ + if$ + } + { + organization "organization" bibinfo.check output + address "address" bibinfo.check output + } + if$ + format.edition output + format.date output + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + new.sentence + format.btitle + "title" output.check + new.sentence + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + format.date "year" output.check + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + new.sentence + format.title output + new.sentence + howpublished "howpublished" bibinfo.check output + format.date output + format.doi output + format.note output + format.eprint output + fin.entry + write.url + empty.misc.check +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + new.sentence + format.btitle + "title" output.check + new.sentence + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + format.date "year" output.check + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} + +FUNCTION {proceedings} +{ output.bibitem + editor empty$ + { organization "organization" bibinfo.check output + } + { format.editors output.nonnull } + if$ + new.sentence + format.btitle "title" output.check + format.bvolume output + format.number.series output + editor empty$ + { publisher empty$ + 'skip$ + { + format.publisher.address output + } + if$ + } + { publisher empty$ + { + format.organization.address output } + { + organization "organization" bibinfo.check output + format.publisher.address output + } + if$ + } + if$ + format.date "year" output.check + format.isbn output + format.issn output + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + new.sentence + format.title + "title" output.check + new.sentence + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + format.date "year" output.check + format.doi output + format.note output + format.eprint output + fin.entry + write.url +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + new.sentence + format.title "title" output.check + format.date output + format.doi output + format.note "note" output.check + format.eprint output + fin.entry + write.url +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ll{ }}{ f{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + nameptr #3 + #1 + = + numnames #3 + > and + { "others" 't := + #1 'namesleft := } + 'skip$ + if$ + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.organization.sort} +{ author empty$ + { organization empty$ + { key empty$ + { "to sort, need author, organization, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { "The " #4 organization chop.word sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.organization.sort} +{ editor empty$ + { organization empty$ + { key empty$ + { "to sort, need editor, organization, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { "The " #4 organization chop.word sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.organization.sort + { type$ "manual" = + 'author.organization.sort + 'author.sort + if$ + } + if$ + } + if$ + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {presort} +% SORT +STRINGS { longest.label } +INTEGERS { number.label longest.label.width } +FUNCTION {initialize.longest.label} +{ "" 'longest.label := + #1 'number.label := + #0 'longest.label.width := +} +FUNCTION {longest.label.pass} +{ number.label int.to.str$ 'label := + number.label #1 + 'number.label := + label width$ longest.label.width > + { label 'longest.label := + label width$ 'longest.label.width := + } + 'skip$ + if$ +} +EXECUTE {initialize.longest.label} +ITERATE {longest.label.pass} +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" longest.label * "}" * + write$ newline$ + "\providecommand{\biburl}[1]{\url{#1}}" +% "\providecommand{\biburl}[1]{#1}" + write$ newline$ + "\providecommand{\urlprefix}{Available from: }" + write$ newline$ + "\expandafter\ifx\csname urlstyle\endcsname\relax" + write$ newline$ + " \providecommand{\doi}[1]{doi:\discretionary{}{}{}#1}\else" + write$ newline$ + " \providecommand{\doi}{doi:\discretionary{}{}{}\begingroup \urlstyle{rm}\Url}\fi" + write$ newline$ +% "\providecommand{\selectlanguage}[1]{\relax}" +% write$ newline$ + "\providecommand{\eprint}[2][]{\biburl{#2}}" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `czech.bst'. From 382a2002575805294cccbfa29bcd2de0abe39d16 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 5 Apr 2019 11:23:43 +0200 Subject: [PATCH 19/65] testbed simplified, methods moved to butils --- jupyter/bc_utils.py | 99 +++++++++++++++++++-- jupyter/testbed.ipynb | 199 ++++++++++++------------------------------ 2 files changed, 149 insertions(+), 149 deletions(-) diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index 3a7fd00..74a48ee 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -11,14 +11,53 @@ class TestDf: train_df = None test_df = None + full_df = None + split = None + state = 0 def __init__(self, init_df): - - split_point = int(len(init_df) * 0.8) - - #splitting the dataset into train (80%) and test (20%) part - self.train_df = init_df.iloc[:split_point] - self.test_df = init_df.iloc[split_point:] + self.state = 0 + self.split = [int(len(init_df) * 0.2), + int(len(init_df) * 0.4), + int(len(init_df) * 0.6), + int(len(init_df) * 0.8)] + self.full_df = init_df + + self.rotate() + + #rotate for new set in cross validation + def rotate(self): + self.state = self.state + 1 + if (self.state == 5): + self.state = 0 + + # below are all the possible splits of full dataset + # T stands for 20% of dataset used for testing + # X stands for the rest 80% of dataset used for training + # TXXXX + if (self.state == 0): + self.test_df = self.full_df.iloc[:self.split[0]] + self.train_df = self.full_df.iloc[self.split[0]:] + # XTXXX + if (self.state == 1): + self.test_df = self.full_df.iloc[self.split[0]:self.split[1]] + self.train_df = self.full_df.iloc[:self.split[0]].append(self.full_df.iloc[self.split[1]:]) + # XXTXX + if (self.state == 2): + self.test_df = self.full_df.iloc[self.split[1]:self.split[2]] + self.train_df = self.full_df.iloc[:self.split[1]].append(self.full_df.iloc[self.split[2]:]) + # XXXTX + if (self.state == 3): + self.test_df = self.full_df.iloc[self.split[2]:self.split[3]] + self.train_df = self.full_df.iloc[:self.split[2]].append(self.full_df.iloc[self.split[3]:]) + # XXXXT + if (self.state == 4): + self.test_df = self.full_df.iloc[self.split[3]:] + self.train_df = self.full_df.iloc[:self.split[3]] + + # returns the internal state of current split + def get_state(): + return self.state #shows plot of given dataframe def show_plot(self): @@ -183,7 +222,7 @@ def delta_medoids_full(df, delta, similarity_measure): if representatives[t] == representatives[t-1]: break - print("delta_medoids_full algorithm ended after " + str(t) + " iterations.") + #print("delta_medoids_full algorithm ended after " + str(t) + " iterations.") return pd.DataFrame(list(representatives[t]), columns=df.columns.values) def estimate_delta(df, similarity_measure): @@ -275,7 +314,7 @@ def greedy_select(df, delta, similarity_measure): # find indexes to drop from dataframe for ix in df.index: - if distance.euclidean(df.loc[ix].values[:], df.loc[current_index].values[:]) <= delta: + if similarity_measure(df.loc[ix].values[:], df.loc[current_index].values[:]) <= delta: rem_indexes = np.append(rem_indexes, ix) # adding selected index to other representatives @@ -288,3 +327,47 @@ def greedy_select(df, delta, similarity_measure): return df +def classifyPoints(ref_df, test_df): + + X_train = ref_df.iloc[:, :-1].values + y_train = ref_df.iloc[:, -1].values + X_test = test_df.iloc[:, :-1].values + y_test = test_df.iloc[:, -1].values + + from sklearn.preprocessing import StandardScaler + scaler = StandardScaler() + scaler.fit(X_train) + + X_train = scaler.transform(X_train) + X_test = scaler.transform(X_test) + + from sklearn.neighbors import KNeighborsClassifier + classifier = KNeighborsClassifier(n_neighbors=1) + classifier.fit(X_train, y_train) + + y_pred = classifier.predict(X_test) + + from sklearn.metrics import classification_report, confusion_matrix + conf_mat = confusion_matrix(y_test, y_pred) + #print(conf_mat) + #print(classification_report(y_test, y_pred)) + return conf_mat + +#input is a precision recall matrix and this methore calculates the ratio +#it is used for comparison of algorithms +def get_hit_miss_rate(matrix): + hit = 0 + miss = 0 + + i = 0 + while i < len(matrix): + j = 0 + while j < len(matrix[i]): + if (i == j): + hit = hit + matrix[i][j] + else: + miss = miss + matrix[i][j] + j = j + 1 + i = i + 1 + + return float(miss)/float(hit) diff --git a/jupyter/testbed.ipynb b/jupyter/testbed.ipynb index c8c166f..60f9661 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -118,7 +118,7 @@ }, "outputs": [], "source": [ - "#creating test datasets for iris\n", + "#creating test datasets\n", "names = set(df['cluster'])\n", "\n", "dataframes = {}\n", @@ -126,7 +126,8 @@ " tmp_df = df[df['cluster'] == name]\n", " dataframes[name] = butils.TestDf(tmp_df)\n", " \n", - "full_test_df = pd.DataFrame()\n", + "#all data for testing\n", + "full_test_df = pd.DataFrame() \n", "for name in names:\n", " full_test_df = full_test_df.append(dataframes[name].test_df)" ] @@ -137,153 +138,50 @@ "metadata": {}, "outputs": [], "source": [ - "#using delta_medoids_full algorithm\n", - "results = {}\n", - "res_len = []\n", - "for name in names:\n", - " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - "\n", - " result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.euclidean) , distance.euclidean)\n", - " result['cluster'] = name\n", - " results[name] = result\n", - " res_len.append(result.shape[0])\n", + "test_res = {\"delta_medoids_full\" : {},\n", + " \"delta_medoids_one_shot\" : {},\n", + " \"random_select\" : {},\n", + " \"greedy_select\" : {}}\n", "\n", - "print(res_len)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#using delta_medoids_one_shot algorithm\n", - "results2 = {}\n", - "res_2_len = []\n", - "for name in names:\n", - " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - " result = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", - " result['cluster'] = name\n", - " results2[name] = result\n", - " res_2_len.append(result.shape[0])\n", - "print(res_2_len)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#using random select algorithm #TODO use same numbers as the best method found\n", - "results3 = {}\n", - "res_3_len = []\n", - "for name, len_num in zip(names, res_len):\n", - " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - " result = butils.random_select(delta_df, len_num)\n", - " result['cluster'] = name\n", - " results3[name] = result\n", - " res_3_len.append(result.shape[0])\n", - "print(res_3_len)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#using greedy algorithm\n", - "results4 = {}\n", - "res_4_len = []\n", - "for name in names:\n", - " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - " result = butils.greedy_select(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", - " result['cluster'] = name\n", - " results4[name] = result\n", - " res_4_len.append(result.shape[0])\n", - "print(res_4_len)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ "#creating training DataFrames for comparing oneshot and full delta medoids algorithm\n", "train_delta_medoids_full = pd.DataFrame()\n", "train_delta_medoids_one_shot = pd.DataFrame()\n", "train_random_selection = pd.DataFrame()\n", "train_greedy_select = pd.DataFrame()\n", "\n", - "for name in names:\n", - " train_delta_medoids_full = train_delta_medoids_full.append(results[name])\n", - " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(results2[name])\n", - " train_random_selection = train_random_selection.append(results3[name])\n", - " train_greedy_select = train_greedy_select.append(results4[name])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def classifyPoints(ref_df, test_df):\n", - "\n", - " X_train = ref_df.iloc[:, :-1].values\n", - " y_train = ref_df.iloc[:, -1].values\n", - " X_test = test_df.iloc[:, :-1].values\n", - " y_test = test_df.iloc[:, -1].values\n", - "\n", - " from sklearn.preprocessing import StandardScaler \n", - " scaler = StandardScaler() \n", - " scaler.fit(X_train)\n", - "\n", - " X_train = scaler.transform(X_train) \n", - " X_test = scaler.transform(X_test)\n", - "\n", - " from sklearn.neighbors import KNeighborsClassifier \n", - " classifier = KNeighborsClassifier(n_neighbors=1)\n", - " classifier.fit(X_train, y_train)\n", "\n", - " y_pred = classifier.predict(X_test)\n", + "for name in names:\n", + " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", + " \n", + " #delta medoids full\n", + " medoids_full_result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.euclidean) , distance.euclidean)\n", + " medoids_full_result['cluster'] = name #setting a cluster name for result\n", + " test_res[\"delta_medoids_full\"][name] = medoids_full_result\n", + " train_delta_medoids_full = train_delta_medoids_full.append(medoids_full_result)\n", "\n", - " from sklearn.metrics import classification_report, confusion_matrix\n", - " conf_mat = confusion_matrix(y_test, y_pred)\n", - " print(conf_mat) \n", - " print(classification_report(y_test, y_pred))\n", - " return conf_mat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matrix_full = classifyPoints(train_delta_medoids_full, full_test_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "matrix_one_shot = classifyPoints(train_delta_medoids_one_shot, full_test_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matrix_random_selection = classifyPoints(train_random_selection, full_test_df)" + " \n", + " #delta medoids one shot\n", + " one_shot_medoids_result = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", + " one_shot_medoids_result['cluster'] = name\n", + " test_res[\"delta_medoids_one_shot\"][name] = one_shot_medoids_result\n", + " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(one_shot_medoids_result)\n", + " \n", + " #random select\n", + " random_select_result = butils.random_select(delta_df, medoids_full_result.shape[0])\n", + " random_select_result['cluster'] = name\n", + " test_res[\"random_select\"][name] = random_select_result\n", + " train_random_selection = train_random_selection.append(random_select_result)\n", + " \n", + " #greedy select\n", + " greedy_select_result = butils.greedy_select(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", + " greedy_select_result['cluster'] = name\n", + " test_res[\"greedy_select\"][name] = greedy_select_result\n", + " train_greedy_select = train_greedy_select.append(greedy_select_result) \n", + " \n", + "matrix_full = butils.classifyPoints(train_delta_medoids_full, full_test_df)\n", + "matrix_one_shot = butils.classifyPoints(train_delta_medoids_one_shot, full_test_df)\n", + "matrix_random_selection = butils.classifyPoints(train_random_selection, full_test_df)\n", + "matrix_greedy_select = butils.classifyPoints(train_greedy_select, full_test_df)" ] }, { @@ -292,7 +190,19 @@ "metadata": {}, "outputs": [], "source": [ - "matrix_greedy_select = classifyPoints(train_greedy_select, full_test_df)" + "precision_results = {\"full_medoids\" : butils.get_hit_miss_rate(matrix_full),\n", + " \"one_shot_medoids\" : butils.get_hit_miss_rate(matrix_one_shot),\n", + " \"random_select\" : butils.get_hit_miss_rate(matrix_random_selection),\n", + " \"greedy_select\" : butils.get_hit_miss_rate(matrix_greedy_select)}\n", + "\n", + "print(\"Rates for each method:\")\n", + "minimal_rate = \"\"\n", + "for key in precision_results:\n", + " print(key + \": \" + str(precision_results[key]))\n", + " if precision_results[key] < minimal_rate:\n", + " minimal_rate = key\n", + " \n", + "print(\"Method that got the best result is \" + minimal_rate)" ] }, { @@ -348,6 +258,13 @@ " ['Delta-Medoids Full', 'Delta-Medoids One Shot', 'Random Selection', 'Greedy Selection'],\n", " [names, names, names, names])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 4d2a7810f0c6ddf668608eb1aee648821bf496a3 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Thu, 25 Apr 2019 09:40:10 +0200 Subject: [PATCH 20/65] text update --- text/hlavac-thesis.bib | 27 ++ text/hlavac-thesis.tex | 610 +++++++++++++++++++++++++++++++---------- 2 files changed, 495 insertions(+), 142 deletions(-) diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib index 454a405..fa70b47 100644 --- a/text/hlavac-thesis.bib +++ b/text/hlavac-thesis.bib @@ -31,3 +31,30 @@ @online{hron2018breaches url={https://blog.avast.com/biggest-data-breaches}, urldate={2019-04-05} } +@article{liebman2015representative, + title={Representative Selection in Nonmetric Datasets}, + author={Liebman, Elad and Chor, Benny and Stone, Peter}, + journal={Applied Artificial Intelligence}, + volume={29}, + number={8}, + pages={807--838}, + year={2015}, + publisher={Taylor \& Francis} +} +@article{liao2013intrusion, + title={Intrusion detection system: A comprehensive review}, + author={Liao, Hung-Jen and Lin, Chun-Hung Richard and Lin, Ying-Chih and Tung, Kuang-Yuan}, + journal={Journal of Network and Computer Applications}, + volume={36}, + number={1}, + pages={16--24}, + year={2013}, + publisher={Elsevier} +} +@book{guttag2016introduction, + title={Introduction to Computation and Programming Using Python: With Application to Understanding Data}, + author={Guttag, John}, + year={2016}, + publisher={MIT Press} +} + diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 0436df0..7ab83d4 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -25,7 +25,12 @@ \usepackage{csquotes} -\usepackage{todonotes} +\usepackage{todonotes} %for TODO notes - can be removed in final + +\usepackage{algorithm, algcompatible} %for for creating algorithm figures +\algnewcommand\INPUT{\item[\textbf{Input:}]} +\algnewcommand\OUTPUT{\item[\textbf{Output:}]} +\algdef{SE}[DOWHILE]{DO}{DOWHILE}{\algorithmicdo}[1]{\algorithmicwhile\ #1}% %\usepackage[main=english,czech]{babel} @@ -49,8 +54,8 @@ \abstractEN{Summarize the contents and contribution of your work in a few sentences in English language.} \abstractCS{V n{\v e}kolika v{\v e}t{\' a}ch shr{\v n}te obsah a p{\v r}{\' i}nos t{\' e}to pr{\' a}ce v {\v c}esk{\' e}m jazyce.} %TODO \placeForDeclarationOfAuthenticity{Prague} -\keywordsCS{Replace with comma-separated list of keywords in Czech.} %TODO -\keywordsEN{Replace with comma-separated list of keywords in English.} %TODO +\keywordsCS{VýbÄ›r reprezentantů, Detekce anomálií, Komunitní clusterování, Systém detekce naruÅ¡ení, $\delta$-medoids, Topologický prostor, Párová podobnost} %TODO +\keywordsEN{Representative selection, Network anomaly detection, Community-based clustering, Intrusion detection system, $\delta$-medoids, Topological space, Pair-wise similarity} %TODO \declarationOfAuthenticityOption{1} %select as appropriate, according to the desired license (integer 1-6) % \website{http://site.example/thesis} %optional thesis URL @@ -63,17 +68,38 @@ \setsecnumdepth{part} \chapter{Introduction} -Even after several decades after the Internet became the most used medium for communication, there are still many issues concerning its security. Businesses all around the world daily connect more of their infrastructure to the Internet thus creating a possibility for attackers to exploit the connected devices for their malicious use. Security engineers are always inventing new methods to stop the attacks and secure their networks. Attackers race to overcome these newly created methods and to gain control of protected data or devices. This race creates a need for much more sophisticated defense mechanisms. \\ - -Multi-layer defense systems are protecting contemporary networks. As attackers overcome one layer, there is another layer waiting for them after it. However, if the succeed to bypass all of them unnoticed a colossal problem can occur. They can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. It can take a long time before the breach is noticed. A similar thing happened in Marriot International child company Starwood last year. Names, emails, addresses and credit card numbers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014\cite{hron2018breaches}. \\ +Even several decades after the Internet became the most used medium for communication, there are still many issues concerning its security. +Businesses all around the world daily connect more of their infrastructure to the Internet thus creating a possibility for attackers to exploit the connected devices for their malicious use. +Security engineers are always inventing new methods to stop the attacks and secure their networks. +Attackers race to overcome these newly created methods and to gain control of protected data or devices. +This race creates a need for much more sophisticated defense mechanisms. \\ + +Multi-layer defense systems are protecting contemporary networks. +As attackers overcome one layer, there is another layer waiting for them after it. +However, if the attackers succeed to bypass all of them unnoticed a huge problem can occur. +They can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. +It can take a long time before the breach is noticed. +A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. +Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. \\ -One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). NBAD compares the current behavior of the network to a model created from previous behavior. Devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. Models are calculated from the data collected. If a significant change in behavior is detected, security personnel are notified, and they can act accordingly. \\ +One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). +NBAD compares the current behavior of the network to a model created from previous behavior. +Devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. +Models are calculated from the data collected. +If a significant change in behavior is detected, security personnel are notified, and they can act accordingly. \\ -It is normal for a company to have up to millions of devices. Straight forward usage of NBAD in these big networks is not sufficient to detect breaches successfully. For example, a single infected device that can be easily missed in the traffic of a bigger company. In order to detect such anomalies in this network, it is needed to focus on smaller parts of it. Clustering based on the behavior of network hosts can be used to separate the network into smaller pieces. \\ - -Cognitive targeted anomaly detection framework from Cisco serves as an example of this clustering. Each cluster that it creates consists of hosts that behave similarly. The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ +It is normal for a company to have up to millions of devices. +Straight forward usage of NBAD in these big networks is not sufficient to detect breaches successfully. +For example, a single infected device that can be easily missed in the traffic of a bigger company. +In order to detect such anomalies in this network, it is needed to focus on smaller parts of it. +Clustering based on the behavior of network hosts can be used to separate the network into smaller pieces. \\ + +Cognitive targeted anomaly detection framework from Cisco serves as an example of such clustering. +Each cluster that it creates consists of hosts that behave similarly. +The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ -Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ +Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. +They were tested and compared on both real and test datasets. \\ (Chapters and sections will be briefly explained here.) @@ -124,137 +150,156 @@ \chapter{Goals} \chapter{Intrusion Detection System} The following chapter focuses on presenting ideas from network security needed to understand the main focus of this thesis. -Different approaches of network intrusion detection systems (IDS) are explained in Section 1. -In Section 2 the scope is narrowed to network anomaly detection IDS. -Section 3 serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. -Methods researched in this thesis were tested on real-life data collected from this framework. \\ - -\section{Types of Intrusion Detection Systems} - +Different approaches of network intrusion detection systems (IDS) are explained in Section 1.1. +In Section 1.2 the scope is narrowed to network anomaly detection IDS, and Section 1.3 delves deeper into the challenges of it. +Section 1.4 serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. +Methods researched in this thesis were tested on real data collected from this framework. \\ + +\section{Types of Intrusion Detection Systems} + An intrusion detection system (IDS) is a security tool designed for identification of unauthorized use or abuse of computer systems by both system insiders and external penetrators\cite{mukherjee1994network}. -IDS that is designed specifically for monitoring computer network is called Network IDS (NIDS). -Few of commonly used NIDS software is listed below: -\begin{itemize} - \item Snort - \item Suricata - \item Bro Network Security Monitor - %\item %TODO Get more examples -\end{itemize} +IDS that is designed specifically for monitoring computer network is called Network IDS (NIDS) as in comparison to host IDS and hybrid IDS. +Host IDS focuses on monitoring the internal state of a computer system (e. g. mainframe computer) and its dynamic behavior. +Hybrid IDS combines different techniques including the ones used in a host and network IDS creating an IDS that can leverage information from each of its parts for better intrusion detection. +Examples of NIDS software are: +\begin{itemize} + \item Snort + \item Suricata + \item Bro Network Security Monitor +\end{itemize} +\todo{citations for each NIDS, website enough?} These systems are used on computer networks to detect and sometimes even prevent attacks that are threats to one of the essential services of such a network. -These basic services are\cite{mukherjee1994network}: -\begin{itemize} - \item Data confidentiality - \item Data and communication integrity - \item Accessibility -\end{itemize} -Attackers, on the other hand, try to disrupt these services by accessing confidential information (snooping), manipulating information (man-in-the-middle attacks) or disabling access to network services (Denial of Service attacks). +These basic services are\cite{mukherjee1994network:1}: +\begin{itemize} + \item Data confidentiality + \item Data and communication integrity + \item Accessibility +\end{itemize} +Attackers, on the other hand, try to disrupt these services by accessing confidential information (snooping), manipulating information (data tampering attacks) or disabling access to network services (Denial of Service attacks). IDS must have multiple components to detect as many of these attacks as is possible. -As each attack kind of intrusion is better detected by a different method, there are several types of IDS: -\begin{itemize} - \item Signature-based (knowledge-based) %TODO put it into sentences with an explanation - \item Anomaly-based (behavior-based) - \item Stateful protocol analysis (specification based) -\end{itemize} %TODO citation -Anomaly-based IDS serves as a foundation for work presented in this thesis. - +As each attack kind of intrusion is better detected by a different method, there are several types of IDS \cite{liao2013intrusion}: +\begin{itemize} + \item \textbf{Signature-based} (knowledge-based): Patterns of known attacks or threats are being compared to captured events for intrusion detection. + \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time and current network behavior is compared to expected (model) behavior for anomalies. + \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. +\end{itemize} +Cognitive targeted anomaly detection framework combines all of these approaches. +This thesis focuses on the behavior-based part of the framework. + \section{Network Anomaly Detection} -Network anomaly detection is a method used in Intrusion Detection Systems (IDS) as explained in previous section. -This method focuses on comparing network host behavior changes in time. +Network anomaly detection is a method used in Intrusion Detection Systems (IDS) as explained in the previous section. +This method focuses on comparing network host behavior changes in time. If a change greater than a certain threshold is observed, network anomaly is detected. In IDS when an anomaly is detected, an alert is created to inform network administrator about what has happened. -This alert can be any kind of message ranging from a syslog message to an email sent to the admin. \\ - -Host behavior is collected from devices in the network. +This alert can be any kind of message ranging from a syslog message to an email sent to the admin. \\ + +Host behavior is collected from devices in the network. There is at least one (although many times multiple) device set up for collecting network flows (using NetFlow protocol). -Flow collection is the most widely used method for gathering data on the network. %TODO citation -One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and number of data in each direction. % [RFC2722] TODO citation +Flow collection is the most widely used method for gathering data on the network. +One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and number of data in each direction. +\todo{[RFC2722] citation} This aggregation of information allows much faster (even real-time) detection of problems on a network as compared to for example deep packet inspection (DPI). -DPI is another method used to detect problems in a network. -It focuses on exploring the payload of each packet. -In today's networks, this method is quickly becoming obsolete as the majority of traffic is encrypted, and therefore it is hard to perform DPI. \\ - -Network flows are sent via NetFlow protocol to one place where they are stored, and detection mechanisms are performed based on the data collected. -Network anomaly detection system creates a model of the behavior of the given network, which serves as a baseline behavior of a given network for a specific time window. -Further traffic is then collected and another model of behavior for another time window is computed. -If the behavior suddenly significantly changes it is considered to be an anomaly that is reported by the IDS. \\ %TODO talk better about time windows - -\section{Challenges of Real-Time Anomaly Detection} -In theory the best way to find all all anomalies is to be able to save all traffic and calculate the model in previous section continuously on for all hosts. -This way there would be a very specific and precise model for each host in the network. -Approaching it this way, knowing all the information about the network it would be very easy to find anomaly in behaviour of each single host with a 100\% precision. -In reality this is not possible because hosts do not generate enough traffic to create a good model of their behavior.a \\ - +DPI is another method used to detect problems in a network. +It focuses on exploring the payload of each packet and is mostly considered unusable. +Not only the majority of traffic is encrypted, but it is also impossible to look into each packet because of the amount of traffic in today's networks. \\ + +Network flows are collected and then sent via NetFlow protocol to one place where they are stored, and evaluated by detection algorithms. +Network anomaly detection system creates a baseline model of the network behavior, which is then compared to actual traffic. +Any sudden change is considered to be an anomaly that is reported by the IDS. \\ + +\section{Challenges of Real-Time Anomaly Detection} +Even if it were possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100% precision. +Not every network host generates enough traffic to create a precise model of its behavior. +Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. \\ +\todo{Explain better, what is the goal of anomaly detection, Grill Thesis} + That is why models are calculated for whole networks where it is possible to create a model that reflects the behavior of the network. -This method is widely used and on smaller (several device) networks it is very efficient. -However considering a big network of thousands of devices in so many information we can easily miss one infected device generating different traffic. -The noise in the collected traffic would be too high. \\ - -To give an example imagine detection an infection one printer in a 10 story office building. -This printer has been infected and added to a botnet to generate traffic for DDoS (Distributed Denial of Service) attacks. +This method is widely used, and on smaller (several devices) networks it is very efficient. +However, considering a big network of thousands of devices in so many information, we can easily miss one infected device generating different traffic. +The noise in the collected traffic would be too high. \\ + +To give an example, imagine detection an infection of a single infected device, such as a printer in a 10 story office building. +This printer, being a part of a botnet, was ordered to generate traffic for DDoS (Distributed Denial of Service) attacks. Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. -If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personel working in the building. \\ - -%TODO Reference article - network-aware behavior clusterin of internet hosts -A way to approach this problem is explained in \cite{xu2011network}. -The model is created by creating bi-partite graphs for comunictaion of hosts in one network prefix. -These graphs are created by looking at hosts from perspective of whom does it communicate with. -From these graphs an adjacency matrix is created. +If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personnel working in the building. \\ + +/todo{comment from Martin - not sure if it is well placed in this section. I believe it is as I want to list a few methods different from ours to tackle the same problem. We can talk about it at the next meeting.} +A way to approach this problem is explained in \cite{xu2011network}. +The model is created by creating bi-partite graphs for communication of hosts in one network prefix. +These graphs are created by looking at hosts from the perspective of whom does it communicate with. +From these graphs, an adjacency matrix is created. How adjacent one host is to another is calculated as a normalized weight of edges in the graph. -This adjacency matrix then serves as a baseline model for anomaly detection. -Assessing each time window of traffic means calculating an adjacency matrix for this timewindow and then comparing to the trained model. -If there is a substantional difference an anomaly is detected. -This method is proven to work, however it suffers from the noise of too many hosts explained before. -Also it might not be a good way to look at the traffic from the perspective of a network prefix, as many times a lot of different devices are on one prefix. -\\ \\ -%Other ways -\\ \\ -%false positives -There is also one more problem in creating a good network anomaly detection system - false positive detection. -A false positive is a network anomaly that is detected and is not an actual anomaly. -For example if we would have our anomaly detection system setup wrong it might detect an anomaly everyday at 8AM when workers come to the office, start their computers and download daily email. -Comparing the time window from before 8AM and after 8AM would not give as any relevant information. -This problem can be mitigated by setting window for which the model is calculated long enough, so that these mistakes does not happen. -In other situations false positive alerts can happen and the goal of a good network anomaly detection system is to keep them as close to 0 as possible. - -\section{Cognitive Targeted Anomaly Detection Framework} - -% Cognitive Threat Analytics +This adjacency matrix then serves as a baseline model for anomaly detection. +Assessing each time window of traffic means calculating an adjacency matrix for this time window and then comparing to the trained model. +If there is a substantial difference, an anomaly is detected. +This method is proven to work; however, it suffers from the noise of too many hosts explained before. +Also, it might not be a good way to look at the traffic from the perspective of a network prefix, as many times a lot of different devices are on one prefix. \\ +\todo{Other ways of detection} + +If an AD system does not mark malicious traffic as an anomaly, it is considered a false negative. +That is another challenge AD systems are struggling with, to keep the number of missed attacks as close to 0 as possible. \\ + +One more problem connected to false negatives are the false positives. +A false positive is a network sample of benign network traffic marked as anomalous by the AD system. +For example, if we would have our anomaly detection system set up wrong, it might detect an anomaly every day at 8 AM when workers come to the office, start their computers and download daily email. +Comparing the time window from before 8 AM and after 8 AM would not give us any relevant information. +This problem can be mitigated by setting a window for which the model is calculated long enough so that these mistakes do not happen. +In other situations, false positive alerts can happen, and the goal of network anomaly detection system is to keep them as close to 0 as possible. \\ + +In conclusion, a good anomaly detection system should detect all anomalies that are somehow connected to malicious behavior while trying to keep the number of false positives alerts as small as possible. +The small number of false positives that are reported is then double-checked in the following layers in the IDS. + +\section{Cognitive Targeted Anomaly Detection Framework} + Algorithms studied in this thesis are tested as a part of Cognitive Targeted Anomaly Detection Framework which is a part of Cognitive Threat Analytics developed and used by Cisco. -This framework successfuly uses community-based clustering on behaviors of each host to separate whole network into smaller groups. -Running anomaly detection for each group then yields significantly better results as compared to traditional whole network approach. -Not only it works better because of commynity-based clustering, but also because of the ability to adapt dynamically as the network changes. -Thus it is able incorporate changes that happen on given network such as adding and removing devices.\\ - -To dive into more detail the method is separated into 2 phases. -Initial phase is used to learn the first state of the network, second ongoing phase starts when initial model is created. -During this ongoing phase the framework is able to dynamically adjust clusters to the current state of the network. \\ - -Initial phase starts by collecting 24 hours of traffic from given network. -Data collected consists of network traffic flows and proxy server logs. -Once the 24-hour period is over, clustering of hosts starts. -Each host is represented by a tuple $h = {S^h, F^h}$, where: -\begin{itemize} - \item $S^h$ represents set of all visited pairs server:port - \item $F^h$ represents the frequency of visits of server:port pairs -\end{itemize} +This framework successfully uses community-based clustering on behaviors of each host to split the whole network into smaller groups. +Running anomaly detection for each group then yields significantly better results as compared to the traditional whole network approach. +Not only it works better because of community-based clustering, but also because of the ability to adapt dynamically as the network changes. +Thus it is able to incorporate changes that happen on a given network such as adding and removing devices.\\ + +The method is separated into 2 phases. +The initial phase is used to learn the first state of the network, and the second ongoing phase starts when the initial model is created. +During this ongoing phase, the framework can dynamically adjust clusters to the current state of the network. \\ + +The initial phase starts by collecting 24 hours of traffic from a given network. +Collected data consists of network traffic flows and proxy server logs. +Once the 24-hour period is over, clustering of hosts starts. +Each host is represented by a tuple $h = {S^h, F^h}$, where: +\begin{itemize} + \item $S^h$ represents set of all visited pairs server:port + \item $F^h$ represents the frequency of visits of server:port pairs +\end{itemize} This frequency-based approach is chosen so that some servers like Facebook or Google do not overwhelm the statistics in the model. -When each host has its representation the community-based clustering algorithm is started using similarity measure of: -$$cos(h, h') = \frac{\sum_{s \in S} F_s^h F_s^{h'}} {\sqrt{\sum_{s \in S} (F_s^h)^2} \sqrt{\sum_{s \in S} (F_s^{h'})^2}}$$ -%TODO read about and explain community based clustering -For clusters that are bigger than 30 hosts representatives are selected. %TODO check if it is really 30 -Each host is then given a random number between 1 and 12 which serves as time to live for that method. %TODO check the range -This time to live is crucial for dynamic changes in the network, because of it clusters can change or even die if they do not maintain their behavior. -Currently 30 hosts are selected at random for each cluster. -In this thesis tests were made to improve this random selection method. - +When each host has its representation the clustering algorithm is started. +A technique called community-based clustering is used to create groups of hosts. +This clustering works for graphs, where communities are more densely connected parts of the graph. +In the words of this NBAD: Those hosts that communicate with similar peers are considered to be in the same community. +Community-based clustering is explained in detail in section 2.3. \todo{link to section}. \\ + +The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. +Similarity measure between hosts \textit{a} and \textit{b} is defined as: +$$cos(a, b) = \frac{\sum_{s \in S} F_s^a F_s^b} {\sqrt{\sum_{s \in S} (F_s^a)^2} \sqrt{\sum_{s \in S} (F_s^b)^2}}$$ +With this similarity measure it is possible to determine cluster of hosts with similar behavior. \\ + +After the clustering is done, only clusters that are bigger than 10 hosts are selected for representative selection. +Smaller clusters are analyzed using a fallback method - host-based anomaly detection. +For the purpose of this work, we do not consider clusters smaller than 10 hosts. \\ + +When clusters are determined, representatives are selected from each cluster. +Currently, $\kappa$ hosts are selected at random for each cluster, where $\kappa$ is selected based on parameters in the whole framework. +In this thesis, tests were made to improve this random selection method. +After that, each host is then given a random number between 1 and 12 which serves as a time to live for that method. +This time to live is crucial for dynamic changes in the network because clusters can change or even die out if they do not maintain their behavior. +This is one of the parameters based on which the number of representatives $\kappa$ is selected. \\ + After the initial training phase is over and the model is established, data continues to be collected. -Every 4 hours another all hosts that are observed are assigned to existing clusters using community-based clustering explained above. -For eaach cluster number serving as time to live is decreased and hosts that reach 0 are removed from the cluster. -Free spaces are filled with hosts selected form hosts that were assigned to the cluster from this 4 hour period. -This process goes on and every 4 hours clusters are updated. +Every 4 hours all hosts that are observed are assigned to existing clusters. +For each cluster, number serving as a time to live is decreased, and hosts that reach 0 are removed from the cluster. +Free spaces are filled with hosts selected form hosts that were assigned to the cluster from these 4 hours. -These clusters are then further used for in anomaly detection. +These clusters are then further used for in anomaly detection. They serve as a foundation for calculating baseline behavior for hosts belonging to it. +If a host is known to belong to a cluster $A$ and its behavior suddenly starts to differ from the behavior of representatives selected for cluster $A$, an anomaly is found and reported further into the NIDS. %========================================CHAPTER================================== @@ -262,47 +307,317 @@ \chapter{Theoretical Overview} % Theoretical background to clustering and non-metric spaces This chapter serves as a theoretical background to explain clustering methods and problematics of non-metric spaces. + +\section{Introduction to Clustering} +As \cite{guttag2016introduction} defines it, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. +Without any prior knowledge of the data, this method looks for structures in feature vectors. +Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. +Variability is defined as $$ variability(c) = \sum_{e \in c} distance(mean(c), e)^2 $$, where \textit{e} is an object from given cluster. +For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. +Dissimilarity is defined as $$ dissimilarity(C) = \sum_{c \in C} variability(c) $$, where C stands for all clusters the data was clustered into. +Given this definition, the best way to cluster every dataset would be to put each object to its cluster, and that would not lead to any reasonable result. +Therefore there is a constraint added for clustering methods. +It can be either the maximum number of clusters or the maximum distance between 2 clusters. \\ + +A distance is a measure that quantifies how far two objects with the same features are. +Many different distance measures are used in clustering. +Several of commonly used ones are: +\begin{itemize} + \item Euclidean distance + \item Minkowski distance + \item Manhattan distance + \item Cosine similarity +\end{itemize} + +A straight forward example of clustering is a method called hierarchical clustering. +Given N objects in a dataset, it creates N clusters - meaning there is a cluster for each object. +Then it looks for two closest clusters and merges them into one. +This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. +This method is a greedy algorithm, and therefore it might not result in globally optimal clustering. +Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$, and therefore it cannot be used in big datasets. \\ + +An example of a much faster clustering algorithm that is also greedy is K-means. \todo{How to cite that this is only taken from the book, even though rewritten and selected for my purposes.} +The 'K' in K-means stands for the number of clusters that we want to get as a result. +That means, to use this algorithm the number of desired clusters has to be known in advance. +K-means randomly chooses \textit{k} centroids in the space of the dataset and then assigns each point to a centroid. +After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. +The algorithm stops when the centroids of clusters stop changing. +Figure X shows the pseudocode of K-means. +\begin{algorithm} + \caption{k-Means pseudocode} + \label{k_mean_pseudocode} + \begin{algorithmic}[1] + \INPUT set of examples $\alpha$ number of clusters $k$ + \STATE Randomly chose $k$ examples from $\alpha$ + \WHILE True + \STATE Create $k$ clusters by assigning each object to the closest centroid + \STATE Compute $k$ new centroids by averaging objects in each cluster + \IF{Centroids do not change} + \STATE \textbf{break} + \ENDIF + \ENDWHILE + \end{algorithmic} +\end{algorithm} + +This algorithm is fast, it has time complexity $\mathcal{O}(k*n*d)$, where n is the number of objects in a dataset and d is the computation time of a distance between 2 points. +It is the most commonly used clustering algorithm as it in most cases converges in a few iterations. +Some of the ideas from it are used in the delta medoids algorithm that is used for clustering in this thesis. \\ + +%summary +The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. +Firstly the number of clusters is not previously known, so choosing k for k-Means is not an option, and secondly, the distance measure used does not form a metric space. +Next section delves deeper into what it means when a measure does not form a metric space. + +\section{Non-Metric Space} \todo{Explain topological spaces} +A metric space is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X$ into $R$ which satisfies the following conditions: +\begin{enumerate} + \item [(i)] $d(x, y) \geq 0$; + \item [(ii)] $d(x, y) = 0 \iff x = y$; + \item [(iii)] $d(x, y) = d(y, x)$ + \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. +\end{enumerate} + +The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above and therefore, it does not form a metric space but a topological space. +A topological space is a \todo{definition} + +K-Means clustering from the previous section cannot be used in topological space as the centroids do not exist. +In many cases, there is no prior knowledge of k, as is the case in this thesis. +A popular method that is used for clustering in non-metric spaces is called community-based clustering. \\ + +Community-based clustering detects communities in the data. +A community is a subset of examples in the data, that is densely connected with each other. +One of the ways how to detect communities in a graph is to create a full-adjacency matrix. +This matrix contains all connections between all nodes in the given graph. +Analyzing the matrix then can tell us about the densities in different parts of graphs.\\ + +Louvain method is a similar approach to clustering when we do not have a simple graph but a set of samples and a pairwise similarity measure. +This method relies on creating a full similarity matrix for the whole dataset and then looking for communities in the data. \\ -\section{Introduction to Clustering} -% Briefly explain what is Clustering and how is it used in machine learning +In Cognitive Targeted Anomaly Detection Framework Louvain method cannot be used as calculating the full similarity matrix has a complexity of $\mathcal(n^2)$, which is impossible to calculate for a large network. +That is why an approximative clustering method is used. +This method iteratively samples network hosts and runs the clustering algorithm on the sampled hosts. +Each iteration creates or updates cluster prototypes. +If the data in the current batch fit into a previously prototyped cluster, they are added to it and the cluster prototype is updated. +If samples differ more than a predefined threshold a new cluster prototype is created. -\section{Non-Metric Space} % jenom pairwise similarity!!! - nemuzeme delat kolecka - neplati geometricka reprezentace okoli % centroid vubec neni v uvahu - casto nejde vubec udelat % Louvian Modularity - sestaveni grafu na zaklade pairwise similarit - to co se pouziva v Threat - modularity clustering, community detection +%========================================CHAPTER================================== + \chapter{Representative Selection} -% Representative selection +This chapter focuses on the idea of finding a representation of clusters. +Clustering huge datasets (millions on of objects) can result in big clusters of several tens of thousands of objects in them. +Computational operations such as assigning new objects to clusters (e. g. k-Nearest Neighbors) are dependent on the number of objects in each cluster or the representation of these clusters. +If it were possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. \\ + +Representation of clusters in Cognitive Targeted Anomaly Detection Framework is precisely a problem where computation on clusters needs to run in real-time, and therefore it is desirable to find a smaller representation of each given cluster. + +\section{Definition of Representative Selection} +While having a large set of examples, representative selection aims to find minimal subset from these examples that carry sufficient information about the whole cluster. +This problem was well defined in \cite{liebman2015representative} and the following definition is taken from that paper. \\ + +Let $S$ be a data set, $d : S \times S \to R +$ be a distance measure (not necessarily a metric), and $\delta$ be a distance threshold below which samples are considered sufficiently similar. +The task is finding a representative subset $C \subset S$that best encapsulates the data. +Two following requirements are imposed on an algorithm for finding a representative subset: +\begin{itemize} + \item \textbf{Requirement 1}: The algorithm must return a subset $C \subset S$ such that for any sample $x \in S$, there exists a sample $c \in C$ satisfying $d(x, c) \le \delta$. + \item \textbf{Requirement 2} : The algorithm cannot rely on a metric representation of the samples in $S$. +\end{itemize} +To compare the quality of different subsets returned by different algorithms, two criteria are measured: +\begin{itemize} + \item \textbf{Criterion 1}: $|C|$ - seeking the smallest possible subset C that satisfies Requirement 1:. + \item \textbf{Criterion 2}: Representative should best fit the data on average. Given representative subsets of equal size, the preference is on the one that minimizes the average distance of samples from their respective representatives. +\end{itemize} +Criteria 1 and 2 are applied on a representative set solution. +In addition, the following desiderata for a representative selection algorithm are expected. +\begin{itemize} + \item \textbf{Desideratum 1}: Stable representative selection algorithms are preferred. Let $C_1$ and $C_2$ be different representative subsets for dataset $S$ obtained by two different runs of the same algorithm. +Stability is defined as the overlap $\frac{|C_1 \cap C_2|}{|C_1 \cup C_2|}$ + The higher the expected overlap is, the more stable the +algorithm is. This desideratum ensures the representative set is robust to +randomization in data ordering or the choices made by the algorithm. + \item \textbf{Desideratum 2}: The algorithm should be efficient and scale +well for large datasets. +\end{itemize} + +This definition of representative selection proble serves well for this paper. + -\section{Approaches to Representative Selection} +\section{Representative Selection in Metric Space} +Representative selection in metric space heavily relies on being able to create centroids. +A centroid is a point in space that is not necessarily part of the cluster but sits in the center of the cluster. +A centroid is found by averaging all points of given cluster thus finding a % popsani jak by se to delalo normalne % stezejni myslenky proc to delame takto +\section{Representative Selection in Arbitrary Space} + %What are the algorithms that can be used for representative selection in non-metric spaces? %Problems that need to be tackled using this method. \chapter{Implementation and Performance Testing of Relevant Methods} +This chapter explains the implementation and testing of selected methods in multiple different scenarios. +In the first section algorithms for selected methods are listed and explained in detail. +The following section serves as a list of used datasets ranging from general to more specific ones. +The last section of this chapter talks about the tests that were run and explains why they were chosen. + +The programming language chosen for implementation was Python 3.7. +It allowed different libraries for data manipulation like Pandas library and also is an excellent choice for testing a prototype as the implementation of different algorithms is not complicated. + \section{Methods Relevant to Cognitive Anomaly Detection} -The following methods were tested and compared as a part of this thesis. + +Based on the previously explained approaches for finding representatives of clusters in non-metric spaces the following methods were chosen, tested and compared as a part of this thesis. \begin{itemize} \item Random selection \item Greedy Selection \item Delta-Medoids One-Shot - \item Delta-Medoids %TODO citations + \item Delta-Medoids \todo{cite here when I am citing it below - what are the best practices for this} \end{itemize} -\subsection{Greedy Selection} %explanation of my method -\subsection{Random Selection} + +\subsection{Random Selection} + +This is the algorithm that serves as a baseline to measure the improvement as it is the method currently used in Cognitive targeted anomaly detection framework. +It simply selects a given number of representatives from the cluster randomly. \\ + +In clusters that overlap a lot, selecting random samples from each cluster leads to undesired noise. +However as experimental results presented in \cite{kopp2018community} in the domain of computer networks the overlap of clusters is not great. +It, of course, depends on each individual network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. \\ -\subsection{Delta-Medoids One-Shot} +For this thesis, the random selection algorithm serves as a baseline in comparing the results to other methods. +The number of samples selected is decided by the number of the full Delta-Medoids algorithm. -\subsection{Delta-Medoids} +\subsection{Greedy Selection} %explanation of my method -What criteria were chosen to compare algoritms? -Algorithms comparison. -Summary on what algorithm was chosen and why is it the best one. +The greedy selection algorithm is a simple straightforward algorithm that selects a random sample from the set as the first representative. +Then it looks for the most dissimilar sample in the rest of the dataset. +Once the desired sample is found, the algorithm removes all neighboring samples with at least the similarity of $\delta$ from the set. +Then it looks for other dissimilar samples until the whole set is covered by thus selected representatives. \\ \todo{pseudocode} + +The speed of this algorithm depends on the sparsity of the given set. +In the worst case scenario, all points would be selected as representatives while always calculating the distance to each remaining sample in the set. +Thus reaching the time complexity of $\mathcal{O}(n^2)$. + +\subsection{$\delta$-Medoids} +The two following algorithms were first eplained in \cite{liebman2015representative}. +They were tested on two different problems that also use clustering in non-metric space - computing distance of two musical segments and comparing trajectories of objects. +As was explained in Chapter 2, the term centroid is not relevant in arbitrary spaces, therefore the term medoid is used. +Medoid is a sample that is part of the cluster and that resembles most of the points in its adjacency. \\ + +$\delta$-Medoids algorithms try to find the least of points that are needed to cover the cluster with only one given constraint of the distance of representatives - $\delta$. +There are two versions of this algorithm. +One-shot version is faster but less precise than the full $\delta$-medoids algorithm. + +\subsubsection{$\delta$-Medoids One-Shot} + +The idea behind this algorithm is to go through the dataset looking for representatives that differ at least by the given parameter $\delta$ from each previously selected representatives. +By taking this approach it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. \\ + +The algorithm is shown in Figure \todo{label and link}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. +The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. + +\begin{algorithm} + \caption{$\delta$-Medoids One-shot} + \label{delta_medoids_one_shot} + \begin{algorithmic}[1] + \INPUT data $x_0$ ... $x_m$, required distance $\delta$ + \STATE Initialize \textit{representatives} = $\emptyset$ + \FOR{$i = 0$ \textbf{to} $m$} + \STATE Initialize \textit{dist} = $\infty$ + \FOR{\textit{rep} in \textit{representatives}} + \IF{$d(x_i, rep) \le dist$} + \STATE $dist = d(x_i, rep)$ + \ENDIF + \ENDFOR + \IF{$dist > \delta$} + \STATE add $x_i$ to \textit{representatives} + \ENDIF + \ENDFOR + \end{algorithmic} +\end{algorithm} + + +Selection of the medoids to represent the cluster is strongly influenced by the choice of the first representative. +In some cases, this one-shot approach could be misleading. + +\subsubsection{$\delta$-Medoids} + +The full version of the algorithm runs a one-shot algorithm multiple times. +Each time it goes through the dataset it selects a better medoid than before. +Once 2 passes through the data do not change any medoid, the algorithm stops. \\ + +This approach is clearly slower, as the main routine from one shot algorithm has to be run multiple times. +\todo{Mention convergence of the algorithm.} +On the other hand, it is able to select better medoids, thus getting rid of the difficult choice of the first representative to select. + +\begin{algorithm} + \caption{$\delta$-Medoids} + \label{delta_medoids_full} + \begin{algorithmic}[1] + \INPUT data $x_0$ ... $x_m$, required distance $\delta$ + \STATE t = 0 + \STATE Initialize $representatives_{t_0} = \emptyset$ + \STATE Initialize \textit{clusters} = $\emptyset$ + \DO + \STATE $t = t + 1$ + \FOR{$i = 0$ \textbf{to} $m$} + \STATE Initialize \textit{dist} = $\infty$ + \STATE Initialize \textit{representative = null} + \FOR{\textit{rep} in \textit{representatives}} + \IF{$d(x_i, rep) \le dist$} + \STATE \textit{representative = rep} + \STATE $dist = d(x_i, rep)$ + \ENDIF + \ENDFOR + \IF{$dist \le \delta$} + \STATE add $x_i$ to $cluster_{representative}$ + \ELSE + \STATE \textit{representative = $x_i$} + \STATE Initialize $cluster_{representative} = \emptyset$ + \STATE ad $x_i$ to $cluster_{representative}$ + \STATE add $cluster_{representative}$ to \textit{clusters} + \ENDIF + \ENDFOR + \STATE Initialize $representatives_t = \emptyset$ + \FOR{\textit{cluster} in \textit{clusters}} + \STATE $representative = \textrm{argmin}_{s \in cluster} (\sum\limits_{x \in cluster}{d(x,s) : d(x,s) \le \delta)}$ + \STATE add \textit{representative} to $representatives_t$ + \ENDFOR + \DOWHILE{$representatives_t = representatives_{t-1}$} + \end{algorithmic} +\end{algorithm} + +%\begin{algorithm} +% \caption{$\delta$-Medoids One-shot PAPER} +% \label{delta_medoids_one_shot} +% \begin{algorithmic}[1] +% \INPUT data $x_0$ ... $x_m$, required distance $\delta$ +% \STATE Initialize \textit{representatives} = $\emptyset$ +% \STATE Initialize \textit{clusters} = $\emptyset$ +% \FOR{$i = 0$ \textbf{to} $m$} +% \STATE Initialize \textit{dist} = $\infty$ +% \STATE Initialize \textit{representative = null} +% \FOR{\textit{rep} in \textit{representatives}} +% \IF{$d(x_i, rep) \le dist$} +% \STATE \textit{representative = rep} +% \STATE $dist = d(x_i, rep)$ +% \ENDIF +% \ENDFOR +% \IF{$dist \le \delta$} +% \STATE add $x_i$ to $cluster_{representative}$ +% \ELSE +% \STATE \textit{representative = $x_i$} +% \STATE Initialize $cluster_{representative} = \emptyset$ +% \STATE ad $x_i$ to $cluster_{representative}$ +% \STATE add $cluster_{representative}$ to \textit{clusters} +% \ENDIF +% \ENDFOR +% \end{algorithmic} +%\end{algorithm} \section{Datasets} The task of cluster representation in non-metric spaces generally is not necessarily connected only to the Security field. @@ -320,8 +635,19 @@ \chapter{Results} \setsecnumdepth{part} \chapter{Conclusion} -Algorithms has been tested. -Which one is the best and under what circumstances? +Literature in both Network Anomaly Detection field and representative selection was studied. +Approaches and algorithms thus found were combined and implemented in prototype program. +Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. \\ + +For testing the selected method datasets were created and gathered from the internet to create a diverse testbed for comparing algorithms. +From the selected algorithms both $\delta$-Medoids One Shot and $\delta$-Medoids outperformed Random selection of representatives, which is the current solution of representatives selection in the framework. +The improvement was at least by 1\% in each dataset, while average improvement was 2\%. \todo{Double check these numbers.} \\ + +\todo{Fine-tune the algorithms for the NBAD field and write here about it if it works.} + +If compared by the requirements from the definition of representative selection problem as we approach it, the following can be concluded. +For fast representative selection that does result in more accurate result than selecting samples randomly, $\delta$-Medoids One Shot algorithm should be used. +If the speed is not of great concern or the datastets have lower tens of thousands of samples, full version of $\delta$ medoids yields the best results. \bibliographystyle{iso690} \bibliography{hlavac-thesis.bib} From 272f51bcafa6d57e4c9bb75e536cb1bcc287b661 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Thu, 25 Apr 2019 16:19:08 +0200 Subject: [PATCH 21/65] added skeleton of testbed section --- text/hlavac-thesis.bib | 6 ++++ text/hlavac-thesis.tex | 64 +++++++++++++++++++++++++++++++++++++++--- 2 files changed, 66 insertions(+), 4 deletions(-) diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib index fa70b47..78c6911 100644 --- a/text/hlavac-thesis.bib +++ b/text/hlavac-thesis.bib @@ -57,4 +57,10 @@ @book{guttag2016introduction year={2016}, publisher={MIT Press} } +@book{stahl2014introduction, + title={Introduction to topology and geometry}, + author={Stahl, Saul and Stenson, Catherine}, + year={2014}, + publisher={John Wiley \& Sons} +} diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 7ab83d4..982e5d2 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -304,7 +304,6 @@ \section{Cognitive Targeted Anomaly Detection Framework} %========================================CHAPTER================================== \chapter{Theoretical Overview} -% Theoretical background to clustering and non-metric spaces This chapter serves as a theoretical background to explain clustering methods and problematics of non-metric spaces. @@ -368,7 +367,8 @@ \section{Introduction to Clustering} Firstly the number of clusters is not previously known, so choosing k for k-Means is not an option, and secondly, the distance measure used does not form a metric space. Next section delves deeper into what it means when a measure does not form a metric space. -\section{Non-Metric Space} \todo{Explain topological spaces} +\section{Clustering on Topological Space} + A metric space is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X$ into $R$ which satisfies the following conditions: \begin{enumerate} \item [(i)] $d(x, y) \geq 0$; @@ -377,8 +377,16 @@ \section{Non-Metric Space} \todo{Explain topological spaces} \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. \end{enumerate} -The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above and therefore, it does not form a metric space but a topological space. -A topological space is a \todo{definition} +The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above and therefore, it does not form a metric space but a more general topological space. This is how \cite{stahl2014introduction} defines it. \\ + +Given any set $S$ a topology on S is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$, and with the following properties: +\begin{enumerate} + \item [(i)] The empty set $\emptyset$ is in $F$. + \item [(ii)] The given set $S$ is in $F$. + \item [(iii)] The intersection of any two sets of $F$ is in $F$. + \item [(iv)] The union of any number of sets of $F$ is in $F$. +\end{enumerate} +The pair ($S, F$) is called a topological space. \\ K-Means clustering from the previous section cannot be used in topological space as the centroids do not exist. In many cases, there is no prior knowledge of k, as is the case in this thesis. @@ -399,6 +407,7 @@ \section{Non-Metric Space} \todo{Explain topological spaces} Each iteration creates or updates cluster prototypes. If the data in the current batch fit into a previously prototyped cluster, they are added to it and the cluster prototype is updated. If samples differ more than a predefined threshold a new cluster prototype is created. +%Cosine similarity represents the difference in the angle of two vectors. % jenom pairwise similarity!!! - nemuzeme delat kolecka - neplati geometricka reprezentace okoli % centroid vubec neni v uvahu - casto nejde vubec udelat @@ -626,6 +635,53 @@ \section{Datasets} \section{Testbed} +The task of cluster representation in non-metric spaces generally is not necessarily connected only to the Security field. +That is why the selected algorithms were tested on artificially created datasets, clustering datasets previously used in literature and on specific data collected from a real network. +Datasets that are needed for testing methods from this thesis have to be labeled. +That means that each sample in the dataset must be assigned into a cluster so that when the representation for each cluster is found and samples are assigned to clusters, it is possible to doublecheck if the assignment was right. +There are not many labeled datasets that have a lot of samples in each cluster and have the right features for testing. \todo{what does it mean right features, how to better write this} +That is why for complex testing datasets from picture recognition field were used for general testing of methods before going into the area of computer networks. + +\subsection{Datasets Created for this Work} + +These datasets were created solely for this thesis and they served as a sanity check for each of the method. +The datasets created are the following: +\begin{itemize} + \item Blobs + \item Blobs 3D + \item Overlap + \item Noisy Circles + \item Noisy Circles 3D +\end{itemize} +All of these datasets are in 3-dimensional space so that they could be easily visualized. \todo{add pictures} +Similarity measure used in these datasets was Euclidean distance. \\ + +The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter X. \todo{Link to chapter} The Overlap dataset consists of 3 clusters in shapes of blocks. Noisy Circles and Noisy Circles 3D are datasets are one of the hardest datasets to represent even by methods that are designed specifically for metric space. +There are two clusters as shown in figure X. \todo{add figure} + +\subsection{Image Recognition Datasets} +One of the best-documented areas of clustering is the area of image recognition. +That is why 2 datasets from this area were chosen for measuring the precision and complexity of each method. Both of them are labeled datasets of features collected from digits written by human hand. \\ + + +\subsection{Real Network Dataset} +Confidential dataset of an anonymized network in one real company was used for finetuning and testing the methods on a real scenario. +This dataset was created from proxy logs in that company following the workflow explained in section x \todo{Link section.}. \\ + +\section{Testbed} +Testing of each method was done in several steps: +Data preparation - creation of pandas dataframes and loading it into special Cluster Class. +Cross-validation of each method. 80\% of samples used for training, 20\% for testing. +Testing was done by kNN k = 1, and creation of confusion matrix while calculating precision and recall. + +The testbed application was implemented in Python 3 using the Jupyter notebook technology. +It consists of 3 steps: +\begin{itemize} + \item Loading data into TestDF + \item Training model on 80\% of dataset sample + \item Testing by k-Nearest Neighbor classification method on the remaining 20\% +\end{itemize} + \chapter{Results} % Testbed explanation - why is it the best etc. From 6209a6b1944ec7bbe84da08d7ae4e22b2a24fc84 Mon Sep 17 00:00:00 2001 From: Vaclav Hlavac Date: Thu, 25 Apr 2019 18:26:22 +0200 Subject: [PATCH 22/65] VH test, zda mohu editovat --- text/hlavac-thesis.tex | 368 +++++++++++++++++++++-------------------- 1 file changed, 185 insertions(+), 183 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 982e5d2..8a13ac2 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -1,3 +1,5 @@ +% History +% 2019-04-25 VH zkousim, zda mohu editovat % arara: xelatex % arara: xelatex % arara: xelatex @@ -33,15 +35,15 @@ \algdef{SE}[DOWHILE]{DO}{DOWHILE}{\algorithmicdo}[1]{\algorithmicwhile\ #1}% %\usepackage[main=english,czech]{babel} - + % % list of acronyms % \usepackage[acronym,nonumberlist,toc,numberedsection=autolabel]{glossaries} % \iflanguage{czech}{\renewcommand*{\acronymname}{Seznam pou{\v z}it{\' y}ch zkratek}}{} % \makeglossaries -% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % +% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % EDIT THIS -% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % +% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % \department{Department of Computer Systems} \title{ Memory efficient cluster representations in non-metric spaces} @@ -81,13 +83,13 @@ \chapter{Introduction} It can take a long time before the breach is noticed. A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. \\ - + One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). NBAD compares the current behavior of the network to a model created from previous behavior. Devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. Models are calculated from the data collected. If a significant change in behavior is detected, security personnel are notified, and they can act accordingly. \\ - + It is normal for a company to have up to millions of devices. Straight forward usage of NBAD in these big networks is not sufficient to detect breaches successfully. For example, a single infected device that can be easily missed in the traffic of a bigger company. @@ -96,8 +98,8 @@ \chapter{Introduction} Cognitive targeted anomaly detection framework from Cisco serves as an example of such clustering. Each cluster that it creates consists of hosts that behave similarly. -The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ - +The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ + Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ @@ -106,12 +108,12 @@ \chapter{Introduction} %The main focus of this work is in representing these clusters found by clustering algorithms. %Finding representation of clusters in Cognitive targeted anomaly detection framework is made more difficult by the fact, that similarity measure used for clustering does not form a metric space. %Therefore the focus on methods that can be used for selecting representatives in non-metric spaces. \\ -% -%Each cluster can have up to several thousands of hosts. +% +%Each cluster can have up to several thousands of hosts. %Algorithms considered in this thesis were selected based on the following expectations. %As the hosts are clustered based on their behavior, it is safe to expect that selecting a subset of the cluster can represent its behavior without losing significant information. %Furthermore having fewer hosts as representatives of each cluster significantly reduce memory usage. \\ - + %Why use clustering methods in network anomaly detection? %Why do we need representative selection? %What are the next sections about? @@ -123,7 +125,7 @@ \chapter{Introduction} %Clustering is also used for Anomaly Detection in computer systems. \par %In this work I collected 5TODO known methods used in different fields for %cluster representation. What these methods have in common is possibility to use -%them in spaces where only generic similarity measure exists (these spaces do +%them in spaces where only generic similarity measure exists (these spaces do %not necessarily need to be metric). \chapter{Goals} @@ -154,84 +156,84 @@ \chapter{Intrusion Detection System} In Section 1.2 the scope is narrowed to network anomaly detection IDS, and Section 1.3 delves deeper into the challenges of it. Section 1.4 serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. Methods researched in this thesis were tested on real data collected from this framework. \\ - -\section{Types of Intrusion Detection Systems} - + +\section{Types of Intrusion Detection Systems} + An intrusion detection system (IDS) is a security tool designed for identification of unauthorized use or abuse of computer systems by both system insiders and external penetrators\cite{mukherjee1994network}. IDS that is designed specifically for monitoring computer network is called Network IDS (NIDS) as in comparison to host IDS and hybrid IDS. Host IDS focuses on monitoring the internal state of a computer system (e. g. mainframe computer) and its dynamic behavior. Hybrid IDS combines different techniques including the ones used in a host and network IDS creating an IDS that can leverage information from each of its parts for better intrusion detection. -Examples of NIDS software are: -\begin{itemize} - \item Snort - \item Suricata - \item Bro Network Security Monitor -\end{itemize} -\todo{citations for each NIDS, website enough?} +Examples of NIDS software are: +\begin{itemize} + \item Snort + \item Suricata + \item Bro Network Security Monitor +\end{itemize} +\todo{citations for each NIDS, website enough?} These systems are used on computer networks to detect and sometimes even prevent attacks that are threats to one of the essential services of such a network. -These basic services are\cite{mukherjee1994network:1}: -\begin{itemize} - \item Data confidentiality - \item Data and communication integrity - \item Accessibility -\end{itemize} +These basic services are\cite{mukherjee1994network:1}: +\begin{itemize} + \item Data confidentiality + \item Data and communication integrity + \item Accessibility +\end{itemize} Attackers, on the other hand, try to disrupt these services by accessing confidential information (snooping), manipulating information (data tampering attacks) or disabling access to network services (Denial of Service attacks). IDS must have multiple components to detect as many of these attacks as is possible. As each attack kind of intrusion is better detected by a different method, there are several types of IDS \cite{liao2013intrusion}: -\begin{itemize} +\begin{itemize} \item \textbf{Signature-based} (knowledge-based): Patterns of known attacks or threats are being compared to captured events for intrusion detection. - \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time and current network behavior is compared to expected (model) behavior for anomalies. - \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. -\end{itemize} -Cognitive targeted anomaly detection framework combines all of these approaches. -This thesis focuses on the behavior-based part of the framework. - -\section{Network Anomaly Detection} + \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time and current network behavior is compared to expected (model) behavior for anomalies. + \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. +\end{itemize} +Cognitive targeted anomaly detection framework combines all of these approaches. +This thesis focuses on the behavior-based part of the framework. + +\section{Network Anomaly Detection} Network anomaly detection is a method used in Intrusion Detection Systems (IDS) as explained in the previous section. -This method focuses on comparing network host behavior changes in time. +This method focuses on comparing network host behavior changes in time. If a change greater than a certain threshold is observed, network anomaly is detected. In IDS when an anomaly is detected, an alert is created to inform network administrator about what has happened. This alert can be any kind of message ranging from a syslog message to an email sent to the admin. \\ - -Host behavior is collected from devices in the network. + +Host behavior is collected from devices in the network. There is at least one (although many times multiple) device set up for collecting network flows (using NetFlow protocol). Flow collection is the most widely used method for gathering data on the network. One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and number of data in each direction. -\todo{[RFC2722] citation} +\todo{[RFC2722] citation} This aggregation of information allows much faster (even real-time) detection of problems on a network as compared to for example deep packet inspection (DPI). -DPI is another method used to detect problems in a network. +DPI is another method used to detect problems in a network. It focuses on exploring the payload of each packet and is mostly considered unusable. Not only the majority of traffic is encrypted, but it is also impossible to look into each packet because of the amount of traffic in today's networks. \\ - + Network flows are collected and then sent via NetFlow protocol to one place where they are stored, and evaluated by detection algorithms. Network anomaly detection system creates a baseline model of the network behavior, which is then compared to actual traffic. -Any sudden change is considered to be an anomaly that is reported by the IDS. \\ - -\section{Challenges of Real-Time Anomaly Detection} +Any sudden change is considered to be an anomaly that is reported by the IDS. \\ + +\section{Challenges of Real-Time Anomaly Detection} Even if it were possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100% precision. Not every network host generates enough traffic to create a precise model of its behavior. Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. \\ -\todo{Explain better, what is the goal of anomaly detection, Grill Thesis} - +\todo{Explain better, what is the goal of anomaly detection, Grill Thesis} + That is why models are calculated for whole networks where it is possible to create a model that reflects the behavior of the network. This method is widely used, and on smaller (several devices) networks it is very efficient. However, considering a big network of thousands of devices in so many information, we can easily miss one infected device generating different traffic. -The noise in the collected traffic would be too high. \\ - +The noise in the collected traffic would be too high. \\ + To give an example, imagine detection an infection of a single infected device, such as a printer in a 10 story office building. This printer, being a part of a botnet, was ordered to generate traffic for DDoS (Distributed Denial of Service) attacks. Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personnel working in the building. \\ - -/todo{comment from Martin - not sure if it is well placed in this section. I believe it is as I want to list a few methods different from ours to tackle the same problem. We can talk about it at the next meeting.} -A way to approach this problem is explained in \cite{xu2011network}. + +/todo{comment from Martin - not sure if it is well placed in this section. I believe it is as I want to list a few methods different from ours to tackle the same problem. We can talk about it at the next meeting.} +A way to approach this problem is explained in \cite{xu2011network}. The model is created by creating bi-partite graphs for communication of hosts in one network prefix. These graphs are created by looking at hosts from the perspective of whom does it communicate with. -From these graphs, an adjacency matrix is created. +From these graphs, an adjacency matrix is created. How adjacent one host is to another is calculated as a normalized weight of edges in the graph. -This adjacency matrix then serves as a baseline model for anomaly detection. +This adjacency matrix then serves as a baseline model for anomaly detection. Assessing each time window of traffic means calculating an adjacency matrix for this time window and then comparing to the trained model. -If there is a substantial difference, an anomaly is detected. +If there is a substantial difference, an anomaly is detected. This method is proven to work; however, it suffers from the noise of too many hosts explained before. Also, it might not be a good way to look at the traffic from the perspective of a network prefix, as many times a lot of different devices are on one prefix. \\ \todo{Other ways of detection} @@ -248,27 +250,27 @@ \section{Challenges of Real-Time Anomaly Detection} In conclusion, a good anomaly detection system should detect all anomalies that are somehow connected to malicious behavior while trying to keep the number of false positives alerts as small as possible. The small number of false positives that are reported is then double-checked in the following layers in the IDS. - -\section{Cognitive Targeted Anomaly Detection Framework} - + +\section{Cognitive Targeted Anomaly Detection Framework} + Algorithms studied in this thesis are tested as a part of Cognitive Targeted Anomaly Detection Framework which is a part of Cognitive Threat Analytics developed and used by Cisco. This framework successfully uses community-based clustering on behaviors of each host to split the whole network into smaller groups. Running anomaly detection for each group then yields significantly better results as compared to the traditional whole network approach. Not only it works better because of community-based clustering, but also because of the ability to adapt dynamically as the network changes. Thus it is able to incorporate changes that happen on a given network such as adding and removing devices.\\ - -The method is separated into 2 phases. + +The method is separated into 2 phases. The initial phase is used to learn the first state of the network, and the second ongoing phase starts when the initial model is created. During this ongoing phase, the framework can dynamically adjust clusters to the current state of the network. \\ - -The initial phase starts by collecting 24 hours of traffic from a given network. -Collected data consists of network traffic flows and proxy server logs. -Once the 24-hour period is over, clustering of hosts starts. -Each host is represented by a tuple $h = {S^h, F^h}$, where: -\begin{itemize} - \item $S^h$ represents set of all visited pairs server:port - \item $F^h$ represents the frequency of visits of server:port pairs -\end{itemize} + +The initial phase starts by collecting 24 hours of traffic from a given network. +Collected data consists of network traffic flows and proxy server logs. +Once the 24-hour period is over, clustering of hosts starts. +Each host is represented by a tuple $h = {S^h, F^h}$, where: +\begin{itemize} + \item $S^h$ represents set of all visited pairs server:port + \item $F^h$ represents the frequency of visits of server:port pairs +\end{itemize} This frequency-based approach is chosen so that some servers like Facebook or Google do not overwhelm the statistics in the model. When each host has its representation the clustering algorithm is started. A technique called community-based clustering is used to create groups of hosts. @@ -286,18 +288,18 @@ \section{Cognitive Targeted Anomaly Detection Framework} For the purpose of this work, we do not consider clusters smaller than 10 hosts. \\ When clusters are determined, representatives are selected from each cluster. -Currently, $\kappa$ hosts are selected at random for each cluster, where $\kappa$ is selected based on parameters in the whole framework. +Currently, $\kappa$ hosts are selected at random for each cluster, where $\kappa$ is selected based on parameters in the whole framework. In this thesis, tests were made to improve this random selection method. -After that, each host is then given a random number between 1 and 12 which serves as a time to live for that method. +After that, each host is then given a random number between 1 and 12 which serves as a time to live for that method. This time to live is crucial for dynamic changes in the network because clusters can change or even die out if they do not maintain their behavior. This is one of the parameters based on which the number of representatives $\kappa$ is selected. \\ - + After the initial training phase is over and the model is established, data continues to be collected. Every 4 hours all hosts that are observed are assigned to existing clusters. For each cluster, number serving as a time to live is decreased, and hosts that reach 0 are removed from the cluster. Free spaces are filled with hosts selected form hosts that were assigned to the cluster from these 4 hours. -These clusters are then further used for in anomaly detection. +These clusters are then further used for in anomaly detection. They serve as a foundation for calculating baseline behavior for hosts belonging to it. If a host is known to belong to a cluster $A$ and its behavior suddenly starts to differ from the behavior of representatives selected for cluster $A$, an anomaly is found and reported further into the NIDS. @@ -306,8 +308,8 @@ \section{Cognitive Targeted Anomaly Detection Framework} \chapter{Theoretical Overview} This chapter serves as a theoretical background to explain clustering methods and problematics of non-metric spaces. - -\section{Introduction to Clustering} + +\section{Introduction to Clustering} As \cite{guttag2016introduction} defines it, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. Without any prior knowledge of the data, this method looks for structures in feature vectors. Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. @@ -315,77 +317,77 @@ \section{Introduction to Clustering} For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. Dissimilarity is defined as $$ dissimilarity(C) = \sum_{c \in C} variability(c) $$, where C stands for all clusters the data was clustered into. Given this definition, the best way to cluster every dataset would be to put each object to its cluster, and that would not lead to any reasonable result. -Therefore there is a constraint added for clustering methods. +Therefore there is a constraint added for clustering methods. It can be either the maximum number of clusters or the maximum distance between 2 clusters. \\ - + A distance is a measure that quantifies how far two objects with the same features are. -Many different distance measures are used in clustering. -Several of commonly used ones are: -\begin{itemize} - \item Euclidean distance - \item Minkowski distance - \item Manhattan distance - \item Cosine similarity -\end{itemize} - +Many different distance measures are used in clustering. +Several of commonly used ones are: +\begin{itemize} + \item Euclidean distance + \item Minkowski distance + \item Manhattan distance + \item Cosine similarity +\end{itemize} + A straight forward example of clustering is a method called hierarchical clustering. Given N objects in a dataset, it creates N clusters - meaning there is a cluster for each object. -Then it looks for two closest clusters and merges them into one. +Then it looks for two closest clusters and merges them into one. This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. This method is a greedy algorithm, and therefore it might not result in globally optimal clustering. Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$, and therefore it cannot be used in big datasets. \\ - + An example of a much faster clustering algorithm that is also greedy is K-means. \todo{How to cite that this is only taken from the book, even though rewritten and selected for my purposes.} The 'K' in K-means stands for the number of clusters that we want to get as a result. That means, to use this algorithm the number of desired clusters has to be known in advance. K-means randomly chooses \textit{k} centroids in the space of the dataset and then assigns each point to a centroid. After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. -The algorithm stops when the centroids of clusters stop changing. -Figure X shows the pseudocode of K-means. -\begin{algorithm} - \caption{k-Means pseudocode} - \label{k_mean_pseudocode} - \begin{algorithmic}[1] - \INPUT set of examples $\alpha$ number of clusters $k$ - \STATE Randomly chose $k$ examples from $\alpha$ - \WHILE True +The algorithm stops when the centroids of clusters stop changing. +Figure X shows the pseudocode of K-means. +\begin{algorithm} + \caption{k-Means pseudocode} + \label{k_mean_pseudocode} + \begin{algorithmic}[1] + \INPUT set of examples $\alpha$ number of clusters $k$ + \STATE Randomly chose $k$ examples from $\alpha$ + \WHILE True \STATE Create $k$ clusters by assigning each object to the closest centroid - \STATE Compute $k$ new centroids by averaging objects in each cluster - \IF{Centroids do not change} - \STATE \textbf{break} - \ENDIF - \ENDWHILE - \end{algorithmic} -\end{algorithm} - + \STATE Compute $k$ new centroids by averaging objects in each cluster + \IF{Centroids do not change} + \STATE \textbf{break} + \ENDIF + \ENDWHILE + \end{algorithmic} +\end{algorithm} + This algorithm is fast, it has time complexity $\mathcal{O}(k*n*d)$, where n is the number of objects in a dataset and d is the computation time of a distance between 2 points. It is the most commonly used clustering algorithm as it in most cases converges in a few iterations. Some of the ideas from it are used in the delta medoids algorithm that is used for clustering in this thesis. \\ - -%summary + +%summary The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. Firstly the number of clusters is not previously known, so choosing k for k-Means is not an option, and secondly, the distance measure used does not form a metric space. Next section delves deeper into what it means when a measure does not form a metric space. - + \section{Clustering on Topological Space} A metric space is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X$ into $R$ which satisfies the following conditions: -\begin{enumerate} - \item [(i)] $d(x, y) \geq 0$; - \item [(ii)] $d(x, y) = 0 \iff x = y$; - \item [(iii)] $d(x, y) = d(y, x)$ - \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. -\end{enumerate} - +\begin{enumerate} + \item [(i)] $d(x, y) \geq 0$; + \item [(ii)] $d(x, y) = 0 \iff x = y$; + \item [(iii)] $d(x, y) = d(y, x)$ + \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. +\end{enumerate} + The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above and therefore, it does not form a metric space but a more general topological space. This is how \cite{stahl2014introduction} defines it. \\ Given any set $S$ a topology on S is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$, and with the following properties: -\begin{enumerate} - \item [(i)] The empty set $\emptyset$ is in $F$. - \item [(ii)] The given set $S$ is in $F$. +\begin{enumerate} + \item [(i)] The empty set $\emptyset$ is in $F$. + \item [(ii)] The given set $S$ is in $F$. \item [(iii)] The intersection of any two sets of $F$ is in $F$. \item [(iv)] The union of any number of sets of $F$ is in $F$. -\end{enumerate} +\end{enumerate} The pair ($S, F$) is called a topological space. \\ K-Means clustering from the previous section cannot be used in topological space as the centroids do not exist. @@ -457,7 +459,7 @@ \section{Definition of Representative Selection} \section{Representative Selection in Metric Space} Representative selection in metric space heavily relies on being able to create centroids. A centroid is a point in space that is not necessarily part of the cluster but sits in the center of the cluster. -A centroid is found by averaging all points of given cluster thus finding a +A centroid is found by averaging all points of given cluster thus finding a % popsani jak by se to delalo normalne % stezejni myslenky proc to delame takto @@ -473,9 +475,9 @@ \chapter{Implementation and Performance Testing of Relevant Methods} The following section serves as a list of used datasets ranging from general to more specific ones. The last section of this chapter talks about the tests that were run and explains why they were chosen. -The programming language chosen for implementation was Python 3.7. +The programming language chosen for implementation was Python 3.7. It allowed different libraries for data manipulation like Pandas library and also is an excellent choice for testing a prototype as the implementation of different algorithms is not complicated. - + \section{Methods Relevant to Cognitive Anomaly Detection} Based on the previously explained approaches for finding representatives of clusters in non-metric spaces the following methods were chosen, tested and compared as a part of this thesis. @@ -487,9 +489,9 @@ \section{Methods Relevant to Cognitive Anomaly Detection} \end{itemize} - -\subsection{Random Selection} - + +\subsection{Random Selection} + This is the algorithm that serves as a baseline to measure the improvement as it is the method currently used in Cognitive targeted anomaly detection framework. It simply selects a given number of representatives from the cluster randomly. \\ @@ -511,7 +513,7 @@ \subsection{Greedy Selection} %explanation of my method In the worst case scenario, all points would be selected as representatives while always calculating the distance to each remaining sample in the set. Thus reaching the time complexity of $\mathcal{O}(n^2)$. -\subsection{$\delta$-Medoids} +\subsection{$\delta$-Medoids} The two following algorithms were first eplained in \cite{liebman2015representative}. They were tested on two different problems that also use clustering in non-metric space - computing distance of two musical segments and comparing trajectories of objects. As was explained in Chapter 2, the term centroid is not relevant in arbitrary spaces, therefore the term medoid is used. @@ -529,25 +531,25 @@ \subsubsection{$\delta$-Medoids One-Shot} The algorithm is shown in Figure \todo{label and link}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. -\begin{algorithm} - \caption{$\delta$-Medoids One-shot} - \label{delta_medoids_one_shot} - \begin{algorithmic}[1] - \INPUT data $x_0$ ... $x_m$, required distance $\delta$ - \STATE Initialize \textit{representatives} = $\emptyset$ - \FOR{$i = 0$ \textbf{to} $m$} - \STATE Initialize \textit{dist} = $\infty$ - \FOR{\textit{rep} in \textit{representatives}} - \IF{$d(x_i, rep) \le dist$} - \STATE $dist = d(x_i, rep)$ - \ENDIF - \ENDFOR - \IF{$dist > \delta$} - \STATE add $x_i$ to \textit{representatives} - \ENDIF - \ENDFOR - \end{algorithmic} -\end{algorithm} +\begin{algorithm} + \caption{$\delta$-Medoids One-shot} + \label{delta_medoids_one_shot} + \begin{algorithmic}[1] + \INPUT data $x_0$ ... $x_m$, required distance $\delta$ + \STATE Initialize \textit{representatives} = $\emptyset$ + \FOR{$i = 0$ \textbf{to} $m$} + \STATE Initialize \textit{dist} = $\infty$ + \FOR{\textit{rep} in \textit{representatives}} + \IF{$d(x_i, rep) \le dist$} + \STATE $dist = d(x_i, rep)$ + \ENDIF + \ENDFOR + \IF{$dist > \delta$} + \STATE add $x_i$ to \textit{representatives} + \ENDIF + \ENDFOR + \end{algorithmic} +\end{algorithm} Selection of the medoids to represent the cluster is strongly influenced by the choice of the first representative. @@ -563,42 +565,42 @@ \subsubsection{$\delta$-Medoids} \todo{Mention convergence of the algorithm.} On the other hand, it is able to select better medoids, thus getting rid of the difficult choice of the first representative to select. -\begin{algorithm} - \caption{$\delta$-Medoids} - \label{delta_medoids_full} - \begin{algorithmic}[1] - \INPUT data $x_0$ ... $x_m$, required distance $\delta$ - \STATE t = 0 - \STATE Initialize $representatives_{t_0} = \emptyset$ - \STATE Initialize \textit{clusters} = $\emptyset$ - \DO - \STATE $t = t + 1$ - \FOR{$i = 0$ \textbf{to} $m$} - \STATE Initialize \textit{dist} = $\infty$ - \STATE Initialize \textit{representative = null} - \FOR{\textit{rep} in \textit{representatives}} - \IF{$d(x_i, rep) \le dist$} - \STATE \textit{representative = rep} - \STATE $dist = d(x_i, rep)$ - \ENDIF - \ENDFOR - \IF{$dist \le \delta$} - \STATE add $x_i$ to $cluster_{representative}$ - \ELSE - \STATE \textit{representative = $x_i$} - \STATE Initialize $cluster_{representative} = \emptyset$ - \STATE ad $x_i$ to $cluster_{representative}$ - \STATE add $cluster_{representative}$ to \textit{clusters} - \ENDIF - \ENDFOR - \STATE Initialize $representatives_t = \emptyset$ - \FOR{\textit{cluster} in \textit{clusters}} +\begin{algorithm} + \caption{$\delta$-Medoids} + \label{delta_medoids_full} + \begin{algorithmic}[1] + \INPUT data $x_0$ ... $x_m$, required distance $\delta$ + \STATE t = 0 + \STATE Initialize $representatives_{t_0} = \emptyset$ + \STATE Initialize \textit{clusters} = $\emptyset$ + \DO + \STATE $t = t + 1$ + \FOR{$i = 0$ \textbf{to} $m$} + \STATE Initialize \textit{dist} = $\infty$ + \STATE Initialize \textit{representative = null} + \FOR{\textit{rep} in \textit{representatives}} + \IF{$d(x_i, rep) \le dist$} + \STATE \textit{representative = rep} + \STATE $dist = d(x_i, rep)$ + \ENDIF + \ENDFOR + \IF{$dist \le \delta$} + \STATE add $x_i$ to $cluster_{representative}$ + \ELSE + \STATE \textit{representative = $x_i$} + \STATE Initialize $cluster_{representative} = \emptyset$ + \STATE ad $x_i$ to $cluster_{representative}$ + \STATE add $cluster_{representative}$ to \textit{clusters} + \ENDIF + \ENDFOR + \STATE Initialize $representatives_t = \emptyset$ + \FOR{\textit{cluster} in \textit{clusters}} \STATE $representative = \textrm{argmin}_{s \in cluster} (\sum\limits_{x \in cluster}{d(x,s) : d(x,s) \le \delta)}$ - \STATE add \textit{representative} to $representatives_t$ - \ENDFOR - \DOWHILE{$representatives_t = representatives_{t-1}$} - \end{algorithmic} -\end{algorithm} + \STATE add \textit{representative} to $representatives_t$ + \ENDFOR + \DOWHILE{$representatives_t = representatives_{t-1}$} + \end{algorithmic} +\end{algorithm} %\begin{algorithm} % \caption{$\delta$-Medoids One-shot PAPER} @@ -615,7 +617,7 @@ \subsubsection{$\delta$-Medoids} % \STATE \textit{representative = rep} % \STATE $dist = d(x_i, rep)$ % \ENDIF -% \ENDFOR +% \ENDFOR % \IF{$dist \le \delta$} % \STATE add $x_i$ to $cluster_{representative}$ % \ELSE @@ -624,7 +626,7 @@ \subsubsection{$\delta$-Medoids} % \STATE ad $x_i$ to $cluster_{representative}$ % \STATE add $cluster_{representative}$ to \textit{clusters} % \ENDIF -% \ENDFOR +% \ENDFOR % \end{algorithmic} %\end{algorithm} @@ -667,7 +669,7 @@ \subsection{Image Recognition Datasets} \subsection{Real Network Dataset} Confidential dataset of an anonymized network in one real company was used for finetuning and testing the methods on a real scenario. This dataset was created from proxy logs in that company following the workflow explained in section x \todo{Link section.}. \\ - + \section{Testbed} Testing of each method was done in several steps: Data preparation - creation of pandas dataframes and loading it into special Cluster Class. @@ -693,7 +695,7 @@ \chapter{Results} \chapter{Conclusion} Literature in both Network Anomaly Detection field and representative selection was studied. Approaches and algorithms thus found were combined and implemented in prototype program. -Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. \\ +Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. \\ For testing the selected method datasets were created and gathered from the internet to create a diverse testbed for comparing algorithms. From the selected algorithms both $\delta$-Medoids One Shot and $\delta$-Medoids outperformed Random selection of representatives, which is the current solution of representatives selection in the framework. From 67e859cb008dfcc496679433bca9c91f67fdca6b Mon Sep 17 00:00:00 2001 From: Vaclav Hlavac Date: Thu, 25 Apr 2019 18:55:52 +0200 Subject: [PATCH 23/65] Upravy na str 9 a 11, oznaceny modre --- text/hlavac-thesis.tex | 30 +++++++++++++++++++++++++----- 1 file changed, 25 insertions(+), 5 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 8a13ac2..81fc40b 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -24,7 +24,7 @@ % \usepackage{amssymb} %additional math symbols \usepackage{dirtree} %directory tree visualisation - +\usepackage{color} %VH for comments \usepackage{csquotes} \usepackage{todonotes} %for TODO notes - can be removed in final @@ -41,6 +41,9 @@ % \iflanguage{czech}{\renewcommand*{\acronymname}{Seznam pou{\v z}it{\' y}ch zkratek}}{} % \makeglossaries +% ------------- Colors abbreviations ------------ +\def\cbl{\color{blue}} %VH + % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % EDIT THIS % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % @@ -280,8 +283,15 @@ \section{Cognitive Targeted Anomaly Detection Framework} The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: -$$cos(a, b) = \frac{\sum_{s \in S} F_s^a F_s^b} {\sqrt{\sum_{s \in S} (F_s^a)^2} \sqrt{\sum_{s \in S} (F_s^b)^2}}$$ -With this similarity measure it is possible to determine cluster of hosts with similar behavior. \\ + +{\cbl +\[ +cos(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}} +\] +}% end cbl +% +With this similarity measure it is possible to determine cluster of hosts with similar behavior. +\\ After the clustering is done, only clusters that are bigger than 10 hosts are selected for representative selection. Smaller clusters are analyzed using a fallback method - host-based anomaly detection. @@ -313,9 +323,19 @@ \section{Introduction to Clustering} As \cite{guttag2016introduction} defines it, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. Without any prior knowledge of the data, this method looks for structures in feature vectors. Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. -Variability is defined as $$ variability(c) = \sum_{e \in c} distance(mean(c), e)^2 $$, where \textit{e} is an object from given cluster. +Variability is defined as +{\cbl +$$ +\mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2 \:, +$$ +}% end cbl +where \textit{e} is an object from given cluster. For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. -Dissimilarity is defined as $$ dissimilarity(C) = \sum_{c \in C} variability(c) $$, where C stands for all clusters the data was clustered into. +Dissimilarity is defined as +{\cbl +$$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability(c)} \:,$$ +}% end cbl +where {\cbl $C$} stands for all clusters the data was clustered into. Given this definition, the best way to cluster every dataset would be to put each object to its cluster, and that would not lead to any reasonable result. Therefore there is a constraint added for clustering methods. It can be either the maximum number of clusters or the maximum distance between 2 clusters. \\ From 33c5d97d0a0f693a54cdadcd664b1fae95d7c27f Mon Sep 17 00:00:00 2001 From: Vaclav Hlavac Date: Thu, 25 Apr 2019 19:01:16 +0200 Subject: [PATCH 24/65] Cervena barva pro komentare JH --- text/hlavac-thesis.tex | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 81fc40b..4ac3f58 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -42,8 +42,8 @@ % \makeglossaries % ------------- Colors abbreviations ------------ -\def\cbl{\color{blue}} %VH - +\def\cbl{\color{blue}} %VH's suggestions +\def\cre{\color{red}} %JH's discussion % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % EDIT THIS % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % @@ -323,17 +323,17 @@ \section{Introduction to Clustering} As \cite{guttag2016introduction} defines it, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. Without any prior knowledge of the data, this method looks for structures in feature vectors. Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. -Variability is defined as +Variability is defined as {\cbl -$$ +$$ \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2 \:, $$ }% end cbl where \textit{e} is an object from given cluster. For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. -Dissimilarity is defined as -{\cbl -$$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability(c)} \:,$$ +Dissimilarity is defined as +{\cbl +$$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability(c)} \:,$$ }% end cbl where {\cbl $C$} stands for all clusters the data was clustered into. Given this definition, the best way to cluster every dataset would be to put each object to its cluster, and that would not lead to any reasonable result. From d2565d009ff5ea81fc6b6ef621e736db80ad81fd Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 26 Apr 2019 12:42:33 +0200 Subject: [PATCH 25/65] comments from VH added to text, few code improvements --- jupyter/bc_utils.py | 138 +++++++----- jupyter/testbed.ipynb | 461 ++++++++++++++++++++++++++++++++-------- text/hlavac-thesis.tex | 466 +++++++++++++++++++---------------------- 3 files changed, 680 insertions(+), 385 deletions(-) diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index 74a48ee..fbcdcac 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -77,6 +77,37 @@ def show_plot(self): label="test_"+str(key), color=test_colors[key]) pyplot.show() +#def delta_medoids_one_shot(df, delta, similarity_measure): +# """Returns subset of input DataFrame, that is a good representation +# of given data. +# +# This is a simplified delta-medoids algorithm. It finds out the +# representatives in one pass through the input data. Final representatives +# depend on the ordering of input data. +# +# :param df: in data +# :type df: pandas.DataFrame +# :param delta: maximum distance of points to be considered similar +# :type delta: float +# :param similarity_measure: similarity function to be used in algorithm +# :type similarity_measure: scipy.spacial.distance +# +# :Example: +# >>> TODO""" +# representatives = np.array(df.iloc[0], ndmin=2) +# +# #here starts RepAssign routine for advanced delta-medoids +# for row in df.iterrows(): +# point = tuple(row[1]) +# +# for rep in representatives: #needs optimalization +# if similarity_measure(point, rep) <= delta: +# break +# else: +# representatives = np.vstack((representatives, point)) +# +# return pd.DataFrame(representatives, columns=df.columns.values) + def delta_medoids_one_shot(df, delta, similarity_measure): """Returns subset of input DataFrame, that is a good representation of given data. @@ -96,57 +127,20 @@ def delta_medoids_one_shot(df, delta, similarity_measure): >>> TODO""" representatives = np.array(df.iloc[0], ndmin=2) - #here starts RepAssign routine for advanced delta-medoids - for row in df.iterrows(): - point = tuple(row[1]) - - for rep in representatives: #needs optimalization - if similarity_measure(point, rep) <= delta: - break - else: - representatives = np.vstack((representatives, point)) - - return pd.DataFrame(representatives, columns=df.columns.values) - -def delta_medoids(df, delta, similarity_measure): - """Returns subset of input DataFrame, that is a good representation - of given data. - - This is a simplified delta-medoids algorithm. It finds out the - representatives in one pass through the input data. Final representatives - depend on the ordering of input data. - - :param df: in data - :type df: pandas.DataFrame - :param delta: maximum distance of points to be considered similar - :type delta: float - :param similarity_measure: similarity function to be used in algorithm - :type similarity_measure: scipy.spacial.distance - - :Example: - >>> TODO""" - clusters = {} - - #here starts RepAssign routine for advanced delta-medoids for row in df.iterrows(): dist = float("inf") - represenative = None - point = tuple(row[1]) - for rep in clusters.keys(): + for rep in representatives: #finding the closest representative to current point if similarity_measure(point, rep) <= dist: - representative = rep dist = similarity_measure(point, rep) - if dist <= delta: - clusters[representative] = np.vstack((clusters[representative], - point)) - else: - clusters[point] = np.array(point, ndmin=2) + if dist > delta: + representatives = np.vstack((representatives, point)) - return pd.DataFrame(clusters.keys(), columns=df.columns.values) + return pd.DataFrame(representatives, columns=df.columns.values) +# this is the argmin calculation for delta_medoids_full algorithm def find_best_cluster_representative(cluster, similarity_measure): #input: np.array(of points) #returns a tuple @@ -182,13 +176,13 @@ def delta_medoids_full(df, delta, similarity_measure): :Example: >>> TODO""" - t = 0 - representatives = {} - representatives[t] = set() + t = 0 #iteration number + representatives = {} #selected representatives + representatives[t] = set() #representatives for given iteration while True: #print("\n=========== running for t = " + str(t) + "============") - clusters = {} + clusters = {} #subclusters inside cluster for item in representatives[t]: clusters[item] = np.array(item, ndmin=2) @@ -299,7 +293,7 @@ def greedy_select(df, delta, similarity_measure): current_index = df.loc[index_list[0]].name # finding index of the most different point else: - for index in index_list: + for index in index_list: #TODO does this copy the list current_index = None similarity_sum = 0 # used to store the biggest sum tmp_sum = 0 # used to store the current sum @@ -371,3 +365,53 @@ def get_hit_miss_rate(matrix): i = i + 1 return float(miss)/float(hit) + +def get_method_results(full_test_df, dataframes, similarity, delta): + test_res = {"delta_medoids_full" : {}, + "delta_medoids_one_shot" : {}, + "random_select" : {}, + "greedy_select" : {}} + + #creating training DataFrames for comparing oneshot and full delta medoids algorithm + train_delta_medoids_full = pd.DataFrame() + train_delta_medoids_one_shot = pd.DataFrame() + train_random_selection = pd.DataFrame() + train_greedy_select = pd.DataFrame() + + for name in set(full_test_df['cluster']): + delta_df = dataframes[name].train_df.iloc[:, :-1] + + if delta == None: #estimating delta if none is set + delta = estimate_delta(delta_df, similarity) + + #delta medoids full + medoids_full_result = delta_medoids_full(delta_df, delta, similarity) + medoids_full_result['cluster'] = name #setting a cluster name for result + test_res["delta_medoids_full"][name] = medoids_full_result + train_delta_medoids_full = train_delta_medoids_full.append(medoids_full_result) + + #delta medoids one shot + one_shot_medoids_result = delta_medoids_one_shot(delta_df, delta, similarity) + one_shot_medoids_result['cluster'] = name + test_res["delta_medoids_one_shot"][name] = one_shot_medoids_result + train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(one_shot_medoids_result) + + #random select + random_select_result = random_select(delta_df, medoids_full_result.shape[0]) + random_select_result['cluster'] = name + test_res["random_select"][name] = random_select_result + train_random_selection = train_random_selection.append(random_select_result) + + #greedy select + greedy_select_result = greedy_select(delta_df, delta, similarity) + greedy_select_result['cluster'] = name + test_res["greedy_select"][name] = greedy_select_result + train_greedy_select = train_greedy_select.append(greedy_select_result) + + matrix_full = classifyPoints(train_delta_medoids_full, full_test_df) + matrix_one_shot = classifyPoints(train_delta_medoids_one_shot, full_test_df) + matrix_random_selection = classifyPoints(train_random_selection, full_test_df) + matrix_greedy_select = classifyPoints(train_greedy_select, full_test_df) + return [matrix_full, matrix_one_shot, matrix_random_selection, matrix_greedy_select] + +#TODO return also sizes of selections to evaluate how many of the dataset percentagewise it is diff --git a/jupyter/testbed.ipynb b/jupyter/testbed.ipynb index 60f9661..f0a33ba 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ "\n", "import bc_utils as butils\n", "\n", - "from scipy.spatial import distance\n", + "from scipy.spatial import distance \n", "from mpl_toolkits import mplot3d\n", "\n", "from sklearn.model_selection import train_test_split \n", @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "scrolled": true }, @@ -52,7 +52,7 @@ "# overlap.csv\n", "# pendigit.csv\n", "\n", - "df = pd.read_csv(\"data/pendigit.csv\", index_col=0)" + "df = pd.read_csv(\"data/noisy_circles_3d.csv\", index_col=0)" ] }, { @@ -66,9 +66,144 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0, x y z cluster\n", + "0 -0.677999 -0.698757 0.0 0\n", + "1 0.931437 0.191391 0.0 0\n", + "3 0.872837 0.375023 0.0 0\n", + "6 0.935534 -0.069950 0.0 0\n", + "7 0.159219 0.971415 0.0 0\n", + "8 -0.611547 0.751418 0.0 0\n", + "13 -0.104486 0.942077 0.0 0\n", + "14 0.967687 -0.174183 0.0 0\n", + "15 0.754242 -0.621617 0.0 0\n", + "18 -1.027807 0.155325 0.0 0\n", + "20 0.071758 0.989000 0.0 0\n", + "21 0.655803 -0.812775 0.0 0\n", + "22 0.758490 0.629618 0.0 0\n", + "23 -0.866656 -0.497841 0.0 0\n", + "27 -0.184218 -0.921755 0.0 0\n", + "31 -0.850261 0.502583 0.0 0\n", + "36 -0.603595 -0.675230 0.0 0\n", + "37 0.120921 -0.969693 0.0 0\n", + "38 -0.699461 -0.700145 0.0 0\n", + "39 -0.829813 0.384497 0.0 0\n", + "43 0.738477 -0.657938 0.0 0\n", + "44 -0.615457 0.768498 0.0 0\n", + "45 -0.378752 -0.893043 0.0 0\n", + "47 0.940516 -0.345081 0.0 0\n", + "52 0.053183 -0.948771 0.0 0\n", + "54 -0.160072 -0.895151 0.0 0\n", + "56 0.912894 -0.342490 0.0 0\n", + "57 -0.441389 -0.890764 0.0 0\n", + "60 -0.734677 -0.666446 0.0 0\n", + "61 0.860849 0.554751 0.0 0\n", + "... ... ... ... ...\n", + "1443 -0.997016 0.387509 0.0 0\n", + "1444 -0.665681 0.794896 0.0 0\n", + "1448 0.513648 0.881750 0.0 0\n", + "1450 0.909708 0.223947 0.0 0\n", + "1453 -0.472384 -0.971927 0.0 0\n", + "1454 0.818149 -0.552201 0.0 0\n", + "1455 0.300139 1.023248 0.0 0\n", + "1456 -0.475618 0.766312 0.0 0\n", + "1457 0.952992 -0.295727 0.0 0\n", + "1458 -0.156673 -0.960558 0.0 0\n", + "1459 -0.072489 -1.029532 0.0 0\n", + "1460 0.565584 -0.871809 0.0 0\n", + "1461 0.968489 -0.416161 0.0 0\n", + "1464 -0.868991 -0.393100 0.0 0\n", + "1467 0.718468 -0.673767 0.0 0\n", + "1471 0.644130 0.753145 0.0 0\n", + "1473 -0.712700 -0.626573 0.0 0\n", + "1477 0.554641 0.831357 0.0 0\n", + "1479 0.856840 0.624508 0.0 0\n", + "1481 -0.157257 -0.989907 0.0 0\n", + "1483 0.045540 1.064526 0.0 0\n", + "1484 0.742861 0.603701 0.0 0\n", + "1485 -0.076098 -0.977182 0.0 0\n", + "1486 0.897281 -0.340274 0.0 0\n", + "1487 -0.878503 0.487946 0.0 0\n", + "1489 0.236070 -0.887971 0.0 0\n", + "1491 0.310445 -0.936192 0.0 0\n", + "1492 -0.679949 0.792224 0.0 0\n", + "1498 0.017197 -0.945138 0.0 0\n", + "1499 0.913779 -0.598842 0.0 0\n", + "\n", + "[750 rows x 4 columns])\n", + "(1, x y z cluster\n", + "2 0.548291 0.0 -0.006017 1\n", + "4 0.435427 0.0 -0.294726 1\n", + "5 -0.536253 0.0 -0.163471 1\n", + "9 0.423040 0.0 -0.268125 1\n", + "10 0.173147 0.0 -0.458363 1\n", + "11 -0.219125 0.0 -0.398849 1\n", + "12 0.096109 0.0 -0.519602 1\n", + "16 -0.043996 0.0 -0.531987 1\n", + "17 0.430365 0.0 -0.355165 1\n", + "19 -0.228548 0.0 0.382529 1\n", + "24 -0.520035 0.0 0.072190 1\n", + "25 0.394909 0.0 0.221529 1\n", + "26 -0.480141 0.0 -0.098754 1\n", + "28 0.101596 0.0 0.524727 1\n", + "29 -0.140615 0.0 -0.420062 1\n", + "30 0.305418 0.0 -0.305836 1\n", + "32 -0.520933 0.0 -0.010492 1\n", + "33 0.157637 0.0 0.523338 1\n", + "34 -0.434189 0.0 0.023918 1\n", + "35 0.316513 0.0 -0.394525 1\n", + "40 -0.450135 0.0 -0.285030 1\n", + "41 0.342891 0.0 -0.455510 1\n", + "42 0.531814 0.0 0.031431 1\n", + "46 -0.469287 0.0 0.170619 1\n", + "48 0.280564 0.0 0.318425 1\n", + "49 -0.375463 0.0 -0.387242 1\n", + "50 0.513356 0.0 0.249786 1\n", + "51 -0.461774 0.0 -0.224138 1\n", + "53 0.070808 0.0 -0.380032 1\n", + "55 -0.027487 0.0 -0.554549 1\n", + "... ... ... ... ...\n", + "1439 0.216548 0.0 0.518864 1\n", + "1440 0.528756 0.0 -0.211615 1\n", + "1441 -0.498062 0.0 0.252568 1\n", + "1445 0.479599 0.0 0.001011 1\n", + "1446 -0.491049 0.0 0.160684 1\n", + "1447 0.418982 0.0 -0.324877 1\n", + "1449 0.389844 0.0 0.224851 1\n", + "1451 -0.320696 0.0 -0.223657 1\n", + "1452 0.195747 0.0 0.454857 1\n", + "1462 -0.158193 0.0 -0.469685 1\n", + "1463 -0.035628 0.0 -0.566708 1\n", + "1465 0.120758 0.0 0.482890 1\n", + "1466 -0.499142 0.0 -0.133691 1\n", + "1468 0.483947 0.0 0.200953 1\n", + "1469 0.504083 0.0 -0.092725 1\n", + "1470 -0.380365 0.0 -0.348349 1\n", + "1472 0.325310 0.0 0.443521 1\n", + "1474 0.432635 0.0 -0.220105 1\n", + "1475 0.509025 0.0 -0.003331 1\n", + "1476 -0.140491 0.0 0.458993 1\n", + "1478 0.382893 0.0 0.180935 1\n", + "1480 0.306591 0.0 0.314238 1\n", + "1482 0.271623 0.0 0.321017 1\n", + "1488 0.361002 0.0 -0.343894 1\n", + "1490 -0.418928 0.0 0.076475 1\n", + "1493 -0.238985 0.0 0.434101 1\n", + "1494 0.222413 0.0 -0.405034 1\n", + "1495 0.473205 0.0 0.034184 1\n", + "1496 0.406588 0.0 0.274089 1\n", + "1497 -0.345188 0.0 -0.358048 1\n", + "\n", + "[750 rows x 4 columns])\n" + ] + } + ], "source": [ "grouped = df.groupby('cluster')\n", "for group in grouped:\n", @@ -77,11 +212,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "colors = {0:'red', 1:'blue', 2:'green'}\n", "\n", @@ -112,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -127,93 +275,19 @@ " dataframes[name] = butils.TestDf(tmp_df)\n", " \n", "#all data for testing\n", - "full_test_df = pd.DataFrame() \n", - "for name in names:\n", - " full_test_df = full_test_df.append(dataframes[name].test_df)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test_res = {\"delta_medoids_full\" : {},\n", - " \"delta_medoids_one_shot\" : {},\n", - " \"random_select\" : {},\n", - " \"greedy_select\" : {}}\n", - "\n", - "#creating training DataFrames for comparing oneshot and full delta medoids algorithm\n", - "train_delta_medoids_full = pd.DataFrame()\n", - "train_delta_medoids_one_shot = pd.DataFrame()\n", - "train_random_selection = pd.DataFrame()\n", - "train_greedy_select = pd.DataFrame()\n", - "\n", + "full_test_df = pd.DataFrame()\n", "\n", "for name in names:\n", - " delta_df = dataframes[name].train_df.iloc[:, :-1]\n", - " \n", - " #delta medoids full\n", - " medoids_full_result = butils.delta_medoids_full(delta_df, butils.estimate_delta(delta_df, distance.euclidean) , distance.euclidean)\n", - " medoids_full_result['cluster'] = name #setting a cluster name for result\n", - " test_res[\"delta_medoids_full\"][name] = medoids_full_result\n", - " train_delta_medoids_full = train_delta_medoids_full.append(medoids_full_result)\n", - "\n", - " \n", - " #delta medoids one shot\n", - " one_shot_medoids_result = butils.delta_medoids_one_shot(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", - " one_shot_medoids_result['cluster'] = name\n", - " test_res[\"delta_medoids_one_shot\"][name] = one_shot_medoids_result\n", - " train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(one_shot_medoids_result)\n", - " \n", - " #random select\n", - " random_select_result = butils.random_select(delta_df, medoids_full_result.shape[0])\n", - " random_select_result['cluster'] = name\n", - " test_res[\"random_select\"][name] = random_select_result\n", - " train_random_selection = train_random_selection.append(random_select_result)\n", - " \n", - " #greedy select\n", - " greedy_select_result = butils.greedy_select(delta_df, butils.estimate_delta(delta_df, distance.euclidean), distance.euclidean)\n", - " greedy_select_result['cluster'] = name\n", - " test_res[\"greedy_select\"][name] = greedy_select_result\n", - " train_greedy_select = train_greedy_select.append(greedy_select_result) \n", - " \n", - "matrix_full = butils.classifyPoints(train_delta_medoids_full, full_test_df)\n", - "matrix_one_shot = butils.classifyPoints(train_delta_medoids_one_shot, full_test_df)\n", - "matrix_random_selection = butils.classifyPoints(train_random_selection, full_test_df)\n", - "matrix_greedy_select = butils.classifyPoints(train_greedy_select, full_test_df)" + " full_test_df = full_test_df.append(dataframes[name].test_df)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "precision_results = {\"full_medoids\" : butils.get_hit_miss_rate(matrix_full),\n", - " \"one_shot_medoids\" : butils.get_hit_miss_rate(matrix_one_shot),\n", - " \"random_select\" : butils.get_hit_miss_rate(matrix_random_selection),\n", - " \"greedy_select\" : butils.get_hit_miss_rate(matrix_greedy_select)}\n", - "\n", - "print(\"Rates for each method:\")\n", - "minimal_rate = \"\"\n", - "for key in precision_results:\n", - " print(key + \": \" + str(precision_results[key]))\n", - " if precision_results[key] < minimal_rate:\n", - " minimal_rate = key\n", - " \n", - "print(\"Method that got the best result is \" + minimal_rate)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "def benchmark_result_plots(matrices, titles, headers):\n", + "def benchmark_result_plots(matrices, titles, headers, rotation_num):\n", " \"\"\"\n", " parameters:\n", " matrices - list of 2d np.array\n", @@ -224,7 +298,7 @@ " ax_list = [ax1, ax2, ax3, ax4]\n", "\n", " for index in range(0, len(matrices)):\n", - " tick_labels = sorted(list(headers[index]))\n", + " tick_labels = list(headers[index])\n", " ax = ax_list[index]\n", " ax.imshow(matrices[index], cmap='binary')\n", "\n", @@ -253,10 +327,219 @@ " weight=\"bold\", fontsize=16)\n", " \n", " ax.set_title(titles[index], fontsize=16)\n", + " plt.savefig('noisy_circles_3d_' + str(rotation_num) + '.png')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rates for each method:\n", + "greedy_select: 0.0\n", + "one_shot_medoids: 0.0\n", + "random_select: 0.0238907849829\n", + "full_medoids: 0.0\n", + "Method that got the best result is full_medoids\n", + "Creating plots.\n", + "\n", + "\n", + "Dataset Rotated!\n", + "\n", + "Rates for each method:\n", + "greedy_select: 0.0\n", + "one_shot_medoids: 0.0\n", + "random_select: 0.0526315789474\n", + "full_medoids: 0.00671140939597\n", + "Method that got the best result is full_medoids\n", + "Creating plots.\n", + "\n", + "\n", + "Dataset Rotated!\n", + "\n", + "Rates for each method:\n", + "greedy_select: 0.0169491525424\n", + "one_shot_medoids: 0.0\n", + "random_select: 0.048951048951\n", + "full_medoids: 0.00671140939597\n", + "Method that got the best result is full_medoids\n", + "Creating plots.\n", + "\n", + "\n", + "Dataset Rotated!\n", + "\n", + "Rates for each method:\n", + "greedy_select: 0.0\n", + "one_shot_medoids: 0.0\n", + "random_select: 0.0526315789474\n", + "full_medoids: 0.00671140939597\n", + "Method that got the best result is full_medoids\n", + "Creating plots.\n", + "\n", + "\n", + "Dataset Rotated!\n", + "\n", + "Rates for each method:\n", + "greedy_select: 0.00334448160535\n", + "one_shot_medoids: 0.0204081632653\n", + "random_select: 0.0830324909747\n", + "full_medoids: 0.0169491525424\n", + "Method that got the best result is full_medoids\n", + "Creating plots.\n", + "\n", + "\n", + "Dataset Rotated!\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i in range(0,5):\n", + " matrices = butils.get_method_results(full_test_df, dataframes, distance.euclidean, None)\n", + "\n", + " matrix_full = matrices[0]\n", + " matrix_one_shot = matrices[1]\n", + " matrix_random_selection = matrices[2]\n", + " matrix_greedy_select = matrices[3]\n", + "\n", + " precision_results = {\"full_medoids\" : butils.get_hit_miss_rate(matrix_full),\n", + " \"one_shot_medoids\" : butils.get_hit_miss_rate(matrix_one_shot),\n", + " \"random_select\" : butils.get_hit_miss_rate(matrix_random_selection),\n", + " \"greedy_select\" : butils.get_hit_miss_rate(matrix_greedy_select)}\n", + "\n", + " print(\"Rates for each method:\")\n", + " evaluation_sum = 0\n", + " minimal_rate = \"\"\n", + " for key in precision_results:\n", + " print(key + \": \" + str(precision_results[key]))\n", + " if precision_results[key] < minimal_rate:\n", + " minimal_rate = key\n", " \n", + " print(\"Method that got the best result is \" + minimal_rate)\n", + " evaluation_sum = evaluation_sum + precision_results[minimal_rate]\n", + " \n", + " print(\"Creating plots.\")\n", + " benchmark_result_plots([matrix_full, matrix_one_shot, matrix_random_selection, matrix_greedy_select],\n", + " ['Delta-Medoids Full', 'Delta-Medoids One Shot', 'Random Selection', 'Greedy Selection'],\n", + " [names, names, names, names], i)\n", + " \n", + " for key in dataframes:\n", + " dataframes[key].rotate()\n", + " print(\"\\n\\nDataset Rotated!\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. \\ - + One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). NBAD compares the current behavior of the network to a model created from previous behavior. Devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. Models are calculated from the data collected. If a significant change in behavior is detected, security personnel are notified, and they can act accordingly. \\ - + It is normal for a company to have up to millions of devices. Straight forward usage of NBAD in these big networks is not sufficient to detect breaches successfully. For example, a single infected device that can be easily missed in the traffic of a bigger company. @@ -101,8 +101,8 @@ \chapter{Introduction} Cognitive targeted anomaly detection framework from Cisco serves as an example of such clustering. Each cluster that it creates consists of hosts that behave similarly. -The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ - +The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ + Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ @@ -111,12 +111,12 @@ \chapter{Introduction} %The main focus of this work is in representing these clusters found by clustering algorithms. %Finding representation of clusters in Cognitive targeted anomaly detection framework is made more difficult by the fact, that similarity measure used for clustering does not form a metric space. %Therefore the focus on methods that can be used for selecting representatives in non-metric spaces. \\ -% -%Each cluster can have up to several thousands of hosts. +% +%Each cluster can have up to several thousands of hosts. %Algorithms considered in this thesis were selected based on the following expectations. %As the hosts are clustered based on their behavior, it is safe to expect that selecting a subset of the cluster can represent its behavior without losing significant information. %Furthermore having fewer hosts as representatives of each cluster significantly reduce memory usage. \\ - + %Why use clustering methods in network anomaly detection? %Why do we need representative selection? %What are the next sections about? @@ -128,25 +128,27 @@ \chapter{Introduction} %Clustering is also used for Anomaly Detection in computer systems. \par %In this work I collected 5TODO known methods used in different fields for %cluster representation. What these methods have in common is possibility to use -%them in spaces where only generic similarity measure exists (these spaces do +%them in spaces where only generic similarity measure exists (these spaces do %not necessarily need to be metric). \chapter{Goals} This work aims to study methods used for the representation of network host behavioral clusters that can be used in Cognitive targeted anomaly detection framework. Behavioral similarity used for clustering in this framework does form a metric space, which creates a requirement for algorithms used for representative selection. \\ + +This thesis should present a method that can be used for the representation of clusters in non-metric space. +This representation serves as cluster prototypes for determining into which group of network hosts do the newly added hosts belong without saving the information about all hosts in that cluster. \\ -As a result of this thesis should be presented a method, that can be used for the representation of given clusters so that further network hosts added to the system can be correctly added to the correct cluster. -Networks monitored by this framework can have up to several tens of thousands of hosts which leads to massive clusters. +Networks monitored by Cognitive targeted anomaly detection framework can have up to several tens of thousands of hosts which leads to massive clusters. If the classification of newly added hosts were done by comparing the new host to all others, the memory and time needed for classification would be unmanageable. -That is why memory efficiency is emphasized. \\ - -To reach this goal general methods used for general cluster representation in non-metric spaces should be studied. +That is why the size of cluster prototypes (number of representatives selected) should be kept as small as possible. \\ + +General methods used for general cluster representation in non-metric spaces should be studied. From these previously studied methods, the best ones will be selected and implemented. -As part of this thesis, there should also be created a benchmark dataset for testing the chosen method. +As part of this thesis, there should also be created as a benchmark dataset for testing the chosen method. A further goal is to test the chosen methods on generally used clustering datasets and the created benchmark dataset. Based on this testing, find the method that suits best for use in this field and incorporates it into the Cognitive targeted anomaly detection framework. -The selected method should be finetuned to get the best possible results in anomaly-detection. +The selected method should be finetuned to get the best possible results in anomaly-detection. \todo{Would this paragraph be more clear if I made a bullet point list?} \setsecnumdepth{all} @@ -154,89 +156,90 @@ \chapter{Goals} \chapter{Intrusion Detection System} -The following chapter focuses on presenting ideas from network security needed to understand the main focus of this thesis. -Different approaches of network intrusion detection systems (IDS) are explained in Section 1.1. -In Section 1.2 the scope is narrowed to network anomaly detection IDS, and Section 1.3 delves deeper into the challenges of it. -Section 1.4 serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. -Methods researched in this thesis were tested on real data collected from this framework. \\ - -\section{Types of Intrusion Detection Systems} - -An intrusion detection system (IDS) is a security tool designed for identification of unauthorized use or abuse of computer systems by both system insiders and external penetrators\cite{mukherjee1994network}. +This chapter focuses on presenting ideas from network security needed to understand the main objective of this thesis. +Different approaches to network intrusion detection systems (IDS) are explained in Section~\ref{sec:ids}. +In Section~\ref{sec:nbad}, the scope is narrowed to network anomaly detection IDS and Section~\ref{sec:challenges}delves deeper into the challenges of it. +Section~\ref{sec:ctadf} serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. +Methods researched in this thesis were tested on real data collected from Cisco's framework. \\ + +\section{Types of Intrusion Detection Systems}\label{sec:ids} + +An intrusion detection system (IDS) is a security tool designed for identification of unauthorized use or abuse of computer systems by both system insiders and external penetrators \cite{mukherjee1994network}. IDS that is designed specifically for monitoring computer network is called Network IDS (NIDS) as in comparison to host IDS and hybrid IDS. Host IDS focuses on monitoring the internal state of a computer system (e. g. mainframe computer) and its dynamic behavior. Hybrid IDS combines different techniques including the ones used in a host and network IDS creating an IDS that can leverage information from each of its parts for better intrusion detection. -Examples of NIDS software are: -\begin{itemize} - \item Snort - \item Suricata - \item Bro Network Security Monitor -\end{itemize} -\todo{citations for each NIDS, website enough?} +Examples of NIDS software are: +\begin{itemize} + \item Snort + \item Suricata + \item Bro Network Security Monitor +\end{itemize} +\todo{citations for each NIDS, website enough?} These systems are used on computer networks to detect and sometimes even prevent attacks that are threats to one of the essential services of such a network. -These basic services are\cite{mukherjee1994network:1}: -\begin{itemize} - \item Data confidentiality - \item Data and communication integrity - \item Accessibility -\end{itemize} -Attackers, on the other hand, try to disrupt these services by accessing confidential information (snooping), manipulating information (data tampering attacks) or disabling access to network services (Denial of Service attacks). -IDS must have multiple components to detect as many of these attacks as is possible. +These basic services are\cite{mukherjee1994network}: +\begin{itemize} + \item Data confidentiality + \item Data and communication integrity + \item Accessibility +\end{itemize} +Attackers try to disrupt these services by accessing confidential information (snooping), manipulating information (data tampering attacks) or disabling access to network services (Denial of Service attacks). +IDS must have multiple components to detect as many of these attacks as possible. As each attack kind of intrusion is better detected by a different method, there are several types of IDS \cite{liao2013intrusion}: -\begin{itemize} +\begin{itemize} \item \textbf{Signature-based} (knowledge-based): Patterns of known attacks or threats are being compared to captured events for intrusion detection. - \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time and current network behavior is compared to expected (model) behavior for anomalies. - \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. -\end{itemize} -Cognitive targeted anomaly detection framework combines all of these approaches. -This thesis focuses on the behavior-based part of the framework. - -\section{Network Anomaly Detection} + \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time and current network behavior is compared to expected (model) behavior for anomalies. + \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. +\end{itemize} +Cognitive targeted anomaly detection framework combines all of these approaches. +This thesis focuses on the behavior-based part of the framework. + +\section{Network Anomaly Detection}\label{sec:nbad} Network anomaly detection is a method used in Intrusion Detection Systems (IDS) as explained in the previous section. -This method focuses on comparing network host behavior changes in time. +This method focuses on comparing network host behavior changes in time. If a change greater than a certain threshold is observed, network anomaly is detected. -In IDS when an anomaly is detected, an alert is created to inform network administrator about what has happened. +In IDS, when an anomaly is detected, an alert is created to inform the network administrator about what has happened. This alert can be any kind of message ranging from a syslog message to an email sent to the admin. \\ - -Host behavior is collected from devices in the network. + +Host behavior is collected from devices in the network. There is at least one (although many times multiple) device set up for collecting network flows (using NetFlow protocol). Flow collection is the most widely used method for gathering data on the network. -One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and number of data in each direction. -\todo{[RFC2722] citation} -This aggregation of information allows much faster (even real-time) detection of problems on a network as compared to for example deep packet inspection (DPI). -DPI is another method used to detect problems in a network. +One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and size of data transferred in each direction. +\todo{[RFC2722] citation} +This aggregation of information enables much faster (even real-time) detection of problems on a network as compared to, for example, deep packet inspection (DPI). +DPI is another method used to detect problems in a network. It focuses on exploring the payload of each packet and is mostly considered unusable. -Not only the majority of traffic is encrypted, but it is also impossible to look into each packet because of the amount of traffic in today's networks. \\ - +Not only the majority of traffic is encrypted, but it is also impossible to look into each packet because of the enormous amount of traffic in today's networks. \\ + Network flows are collected and then sent via NetFlow protocol to one place where they are stored, and evaluated by detection algorithms. -Network anomaly detection system creates a baseline model of the network behavior, which is then compared to actual traffic. -Any sudden change is considered to be an anomaly that is reported by the IDS. \\ - -\section{Challenges of Real-Time Anomaly Detection} -Even if it were possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100% precision. +Network anomaly detection system creates a baseline model of the network behavior, which is then compared to the actual traffic. +Any sudden change is considered to be an anomaly that is reported by the IDS. \\ + +\section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} +Even if it were possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100\% precision. Not every network host generates enough traffic to create a precise model of its behavior. Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. \\ -\todo{Explain better, what is the goal of anomaly detection, Grill Thesis} - +\todo{Explain better, what is the goal of anomaly detection, Grill Thesis} + That is why models are calculated for whole networks where it is possible to create a model that reflects the behavior of the network. This method is widely used, and on smaller (several devices) networks it is very efficient. -However, considering a big network of thousands of devices in so many information, we can easily miss one infected device generating different traffic. -The noise in the collected traffic would be too high. \\ - -To give an example, imagine detection an infection of a single infected device, such as a printer in a 10 story office building. +However, capturing flow data from a big network of thousands of devices can result in too much information. +One can easily miss one infected device generating different traffic. +The noise in the collected data is too high. \\ + +To give an example, imagine detection of a single infected device, such as a printer in a 10 story office building. This printer, being a part of a botnet, was ordered to generate traffic for DDoS (Distributed Denial of Service) attacks. Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personnel working in the building. \\ - -/todo{comment from Martin - not sure if it is well placed in this section. I believe it is as I want to list a few methods different from ours to tackle the same problem. We can talk about it at the next meeting.} -A way to approach this problem is explained in \cite{xu2011network}. + +/todo{MK - WrongPlacement. I want to list a few methods different from ours to tackle the same problem.} +A way to approach this problem is explained in \cite{xu2011network}. The model is created by creating bi-partite graphs for communication of hosts in one network prefix. These graphs are created by looking at hosts from the perspective of whom does it communicate with. -From these graphs, an adjacency matrix is created. +From these graphs, an adjacency matrix is created. How adjacent one host is to another is calculated as a normalized weight of edges in the graph. -This adjacency matrix then serves as a baseline model for anomaly detection. +This adjacency matrix then serves as a baseline model for anomaly detection. Assessing each time window of traffic means calculating an adjacency matrix for this time window and then comparing to the trained model. -If there is a substantial difference, an anomaly is detected. +If there is a substantial difference, an anomaly is detected. This method is proven to work; however, it suffers from the noise of too many hosts explained before. Also, it might not be a good way to look at the traffic from the perspective of a network prefix, as many times a lot of different devices are on one prefix. \\ \todo{Other ways of detection} @@ -254,28 +257,29 @@ \section{Challenges of Real-Time Anomaly Detection} In conclusion, a good anomaly detection system should detect all anomalies that are somehow connected to malicious behavior while trying to keep the number of false positives alerts as small as possible. The small number of false positives that are reported is then double-checked in the following layers in the IDS. -\section{Cognitive Targeted Anomaly Detection Framework} - +\section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} + Algorithms studied in this thesis are tested as a part of Cognitive Targeted Anomaly Detection Framework which is a part of Cognitive Threat Analytics developed and used by Cisco. -This framework successfully uses community-based clustering on behaviors of each host to split the whole network into smaller groups. +This framework successfully uses community-based clustering on the behavior of each host. +The aim is to split the whole network into smaller groups. Running anomaly detection for each group then yields significantly better results as compared to the traditional whole network approach. Not only it works better because of community-based clustering, but also because of the ability to adapt dynamically as the network changes. Thus it is able to incorporate changes that happen on a given network such as adding and removing devices.\\ - -The method is separated into 2 phases. + +The method is separated into two phases. The initial phase is used to learn the first state of the network, and the second ongoing phase starts when the initial model is created. During this ongoing phase, the framework can dynamically adjust clusters to the current state of the network. \\ - -The initial phase starts by collecting 24 hours of traffic from a given network. -Collected data consists of network traffic flows and proxy server logs. -Once the 24-hour period is over, clustering of hosts starts. -Each host is represented by a tuple $h = {S^h, F^h}$, where: -\begin{itemize} - \item $S^h$ represents set of all visited pairs server:port - \item $F^h$ represents the frequency of visits of server:port pairs -\end{itemize} + +The initial phase starts by collecting 24 hours of traffic from a given network. +Collected data consists of network traffic flows and proxy server logs. +Once the 24-hour period is over, clustering of hosts starts. +Each host is represented by a tuple $h = {S^h, F^h}$, where: +\begin{itemize} + \item $S^h$ represents set of all visited pairs server:port + \item $F^h$ represents the frequency of visits of server:port pairs +\end{itemize} This frequency-based approach is chosen so that some servers like Facebook or Google do not overwhelm the statistics in the model. -When each host has its representation the clustering algorithm is started. +When each host has its representation, the clustering algorithm is started. A technique called community-based clustering is used to create groups of hosts. This clustering works for graphs, where communities are more densely connected parts of the graph. In the words of this NBAD: Those hosts that communicate with similar peers are considered to be in the same community. @@ -283,33 +287,26 @@ \section{Cognitive Targeted Anomaly Detection Framework} The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: - -{\cbl -\[ -cos(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}} -\] -}% end cbl -% -With this similarity measure it is possible to determine cluster of hosts with similar behavior. -\\ +$$cos(a, b) = \frac{\sum_{s \in S} F_s^a F_s^b} {\sqrt{\sum_{s \in S} (F_s^a)^2} \sqrt{\sum_{s \in S} (F_s^b)^2}}$$ +With this similarity measure it is possible to determine cluster of hosts with similar behavior. \\ After the clustering is done, only clusters that are bigger than 10 hosts are selected for representative selection. Smaller clusters are analyzed using a fallback method - host-based anomaly detection. For the purpose of this work, we do not consider clusters smaller than 10 hosts. \\ When clusters are determined, representatives are selected from each cluster. -Currently, $\kappa$ hosts are selected at random for each cluster, where $\kappa$ is selected based on parameters in the whole framework. +Currently, $\kappa$ hosts are selected at random for each cluster, where $\kappa$ is selected based on parameters in the whole framework. In this thesis, tests were made to improve this random selection method. -After that, each host is then given a random number between 1 and 12 which serves as a time to live for that method. +After that, each host is then given a random number between 1 and 12 which serves as a time to live for that method. This time to live is crucial for dynamic changes in the network because clusters can change or even die out if they do not maintain their behavior. This is one of the parameters based on which the number of representatives $\kappa$ is selected. \\ - + After the initial training phase is over and the model is established, data continues to be collected. Every 4 hours all hosts that are observed are assigned to existing clusters. For each cluster, number serving as a time to live is decreased, and hosts that reach 0 are removed from the cluster. Free spaces are filled with hosts selected form hosts that were assigned to the cluster from these 4 hours. -These clusters are then further used for in anomaly detection. +These clusters are then further used for in anomaly detection. They serve as a foundation for calculating baseline behavior for hosts belonging to it. If a host is known to belong to a cluster $A$ and its behavior suddenly starts to differ from the behavior of representatives selected for cluster $A$, an anomaly is found and reported further into the NIDS. @@ -317,99 +314,94 @@ \section{Cognitive Targeted Anomaly Detection Framework} \chapter{Theoretical Overview} -This chapter serves as a theoretical background to explain clustering methods and problematics of non-metric spaces. - -\section{Introduction to Clustering} +This chapter serves as a theoretical background to explain clustering methods and the topic of non-metric spaces. + +\section{Introduction to Clustering}\label{sec:clust_intro} As \cite{guttag2016introduction} defines it, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. Without any prior knowledge of the data, this method looks for structures in feature vectors. Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. -Variability is defined as -{\cbl -$$ -\mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2 \:, -$$ -}% end cbl -where \textit{e} is an object from given cluster. +Variability is defined as $$ variability(c) = \sum_{e \in c} distance(mean(c), e)^2\:,$$ where \textit{e} is an object from given cluster. For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. -Dissimilarity is defined as -{\cbl -$$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability(c)} \:,$$ -}% end cbl -where {\cbl $C$} stands for all clusters the data was clustered into. -Given this definition, the best way to cluster every dataset would be to put each object to its cluster, and that would not lead to any reasonable result. -Therefore there is a constraint added for clustering methods. -It can be either the maximum number of clusters or the maximum distance between 2 clusters. \\ - -A distance is a measure that quantifies how far two objects with the same features are. -Many different distance measures are used in clustering. -Several of commonly used ones are: -\begin{itemize} - \item Euclidean distance - \item Minkowski distance - \item Manhattan distance - \item Cosine similarity -\end{itemize} - -A straight forward example of clustering is a method called hierarchical clustering. -Given N objects in a dataset, it creates N clusters - meaning there is a cluster for each object. -Then it looks for two closest clusters and merges them into one. +Dissimilarity is defined as $$ dissimilarity(C) = \sum_{c \in C} variability(c)\:,$$ where C stands for all clusters the data was clustered into. +Given this definition, the best way to cluster every dataset would be to put each object to its cluster. +That would not lead to any reasonable result. +Therefore there is a constraint added for clustering methods. +It can be either the maximum number of clusters or the maximum distance between two clusters. \\ + +The distance is a measure that quantifies how far two objects with the same features are. +Many different distance measures are used in clustering. +Several of commonly used ones are: +\begin{itemize} + \item Euclidean distance + \item Minkowski distance + \item Manhattan distance + \item Cosine similarity +\end{itemize} \todo{Add formulas.} +\todo{kulback-leibler add?} + +A straightforward example of clustering is a method called hierarchical clustering. +Given $N$ objects in a dataset, it creates $N$ clusters - meaning there is a cluster for each object. +The method looks for two closest clusters and merges them into one. This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. This method is a greedy algorithm, and therefore it might not result in globally optimal clustering. -Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$, and therefore it cannot be used in big datasets. \\ - -An example of a much faster clustering algorithm that is also greedy is K-means. \todo{How to cite that this is only taken from the book, even though rewritten and selected for my purposes.} -The 'K' in K-means stands for the number of clusters that we want to get as a result. -That means, to use this algorithm the number of desired clusters has to be known in advance. -K-means randomly chooses \textit{k} centroids in the space of the dataset and then assigns each point to a centroid. +Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$. +Therefore, it cannot be used in big datasets. \\ + +An example of a much faster clustering algorithm that is also greedy is $K$-means. \todo{How to cite that this is only taken from the book, even though rewritten and selected for my purposes.} +The '$K$' in $K$-means stands for the number of clusters that we want to get as a result. +To use this algorithm the number of desired clusters has to be known in advance. +$K$-means randomly chooses $K$ centroids in the space of the dataset and then assigns each point to a centroid. After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. -The algorithm stops when the centroids of clusters stop changing. -Figure X shows the pseudocode of K-means. -\begin{algorithm} - \caption{k-Means pseudocode} - \label{k_mean_pseudocode} - \begin{algorithmic}[1] - \INPUT set of examples $\alpha$ number of clusters $k$ - \STATE Randomly chose $k$ examples from $\alpha$ - \WHILE True +The algorithm stops when the centroids of clusters stop changing. +Figure~\ref{alg:kmeans} shows the pseudocode of $K$-means. +\begin{algorithm}\label{alg:kmeans} + \caption{k-Means pseudocode} + \label{k_mean_pseudocode} + \begin{algorithmic}[1] + \INPUT set of examples $\alpha$ number of clusters $k$ + \STATE Randomly chose $k$ examples from $\alpha$ + \WHILE True \STATE Create $k$ clusters by assigning each object to the closest centroid - \STATE Compute $k$ new centroids by averaging objects in each cluster - \IF{Centroids do not change} - \STATE \textbf{break} - \ENDIF - \ENDWHILE - \end{algorithmic} -\end{algorithm} - -This algorithm is fast, it has time complexity $\mathcal{O}(k*n*d)$, where n is the number of objects in a dataset and d is the computation time of a distance between 2 points. + \STATE Compute $k$ new centroids by averaging objects in each cluster + \IF{Centroids do not change} + \STATE \textbf{break} + \ENDIF + \ENDWHILE + \end{algorithmic} +\end{algorithm} + +This algorithm is fast, it has time complexity $\mathcal{O}(k*n*d)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. It is the most commonly used clustering algorithm as it in most cases converges in a few iterations. Some of the ideas from it are used in the delta medoids algorithm that is used for clustering in this thesis. \\ - -%summary + +%summary The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. -Firstly the number of clusters is not previously known, so choosing k for k-Means is not an option, and secondly, the distance measure used does not form a metric space. +Firstly the number of clusters is not previously known, so choosing $K$ for $K$-means is not an option, and secondly, the distance measure used does not form a metric space. Next section delves deeper into what it means when a measure does not form a metric space. - -\section{Clustering on Topological Space} + +\section{Clustering on Topological Space}\label{sec:cluster_topo} A metric space is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X$ into $R$ which satisfies the following conditions: -\begin{enumerate} - \item [(i)] $d(x, y) \geq 0$; - \item [(ii)] $d(x, y) = 0 \iff x = y$; - \item [(iii)] $d(x, y) = d(y, x)$ - \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. -\end{enumerate} - +\begin{enumerate} + \item [(i)] $d(x, y) \geq 0$; + \item [(ii)] $d(x, y) = 0 \iff x = y$; + \item [(iii)] $d(x, y) = d(y, x)$ + \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. +\end{enumerate} + The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above and therefore, it does not form a metric space but a more general topological space. This is how \cite{stahl2014introduction} defines it. \\ Given any set $S$ a topology on S is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$, and with the following properties: -\begin{enumerate} - \item [(i)] The empty set $\emptyset$ is in $F$. - \item [(ii)] The given set $S$ is in $F$. +\begin{enumerate} + \item [(i)] The empty set $\emptyset$ is in $F$. + \item [(ii)] The given set $S$ is in $F$. \item [(iii)] The intersection of any two sets of $F$ is in $F$. \item [(iv)] The union of any number of sets of $F$ is in $F$. -\end{enumerate} +\end{enumerate} The pair ($S, F$) is called a topological space. \\ +\todo{explain about neighborhoods} + K-Means clustering from the previous section cannot be used in topological space as the centroids do not exist. In many cases, there is no prior knowledge of k, as is the case in this thesis. A popular method that is used for clustering in non-metric spaces is called community-based clustering. \\ @@ -438,18 +430,20 @@ \section{Clustering on Topological Space} %========================================CHAPTER================================== \chapter{Representative Selection} + This chapter focuses on the idea of finding a representation of clusters. Clustering huge datasets (millions on of objects) can result in big clusters of several tens of thousands of objects in them. -Computational operations such as assigning new objects to clusters (e. g. k-Nearest Neighbors) are dependent on the number of objects in each cluster or the representation of these clusters. +Computational operations such as assigning new objects to clusters (e. g. $K$-Nearest Neighbors) are dependent on the number of objects in each cluster or the representation of these clusters. If it were possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. \\ -Representation of clusters in Cognitive Targeted Anomaly Detection Framework is precisely a problem where computation on clusters needs to run in real-time, and therefore it is desirable to find a smaller representation of each given cluster. +Representation of clusters in Cognitive targeted anomaly detection framework is precisely a problem where computation on clusters needs to run in real-time, and therefore it is desirable to find a smaller representation of each given cluster. -\section{Definition of Representative Selection} +\section{Definition of Representative Selection}\label{sec:def_rep_selection} While having a large set of examples, representative selection aims to find minimal subset from these examples that carry sufficient information about the whole cluster. -This problem was well defined in \cite{liebman2015representative} and the following definition is taken from that paper. \\ +This problem was well defined in \cite{liebman2015representative} and the following definition is taken from that paper. -Let $S$ be a data set, $d : S \times S \to R +$ be a distance measure (not necessarily a metric), and $\delta$ be a distance threshold below which samples are considered sufficiently similar. + +Let $S$ be a data set, $d : S \times S \to \mathbb{R} +$ be a distance measure (not necessarily a metric), and $\delta$ be a distance threshold below which samples are considered sufficiently similar. The task is finding a representative subset $C \subset S$that best encapsulates the data. Two following requirements are imposed on an algorithm for finding a representative subset: \begin{itemize} @@ -475,54 +469,52 @@ \section{Definition of Representative Selection} This definition of representative selection proble serves well for this paper. - -\section{Representative Selection in Metric Space} -Representative selection in metric space heavily relies on being able to create centroids. +\section{Representative Selection in a Metric Space}\label{sec:rep_select_metric} +Representative selection in metric space relies heavily on being able to create centroids. A centroid is a point in space that is not necessarily part of the cluster but sits in the center of the cluster. -A centroid is found by averaging all points of given cluster thus finding a +A centroid is found by averaging all points of given cluster thus finding a % popsani jak by se to delalo normalne % stezejni myslenky proc to delame takto -\section{Representative Selection in Arbitrary Space} +\section{Representative Selection in an Arbitrary Space}\label{sec:rep_select_arbitrary} %What are the algorithms that can be used for representative selection in non-metric spaces? %Problems that need to be tackled using this method. +%========================================CHAPTER================================== + \chapter{Implementation and Performance Testing of Relevant Methods} This chapter explains the implementation and testing of selected methods in multiple different scenarios. -In the first section algorithms for selected methods are listed and explained in detail. +In the first section, algorithms for selected methods are listed and explained in detail. The following section serves as a list of used datasets ranging from general to more specific ones. The last section of this chapter talks about the tests that were run and explains why they were chosen. -The programming language chosen for implementation was Python 3.7. +The programming language chosen for implementation was Python 3.7. It allowed different libraries for data manipulation like Pandas library and also is an excellent choice for testing a prototype as the implementation of different algorithms is not complicated. - -\section{Methods Relevant to Cognitive Anomaly Detection} - -Based on the previously explained approaches for finding representatives of clusters in non-metric spaces the following methods were chosen, tested and compared as a part of this thesis. + +\section{Methods Relevant to Cognitive Anomaly Detection}\label{sec:relevant_methods} +Based on the previously explained approaches for finding representatives of clusters in non-metric, spaces the following methods were chosen, tested and compared as a part of this thesis. \begin{itemize} \item Random selection \item Greedy Selection \item Delta-Medoids One-Shot - \item Delta-Medoids \todo{cite here when I am citing it below - what are the best practices for this} + \item Delta-Medoids \todo{citations} \end{itemize} - - -\subsection{Random Selection} - +\subsection{Random Selection}\label{subsec:random_select} + This is the algorithm that serves as a baseline to measure the improvement as it is the method currently used in Cognitive targeted anomaly detection framework. It simply selects a given number of representatives from the cluster randomly. \\ In clusters that overlap a lot, selecting random samples from each cluster leads to undesired noise. -However as experimental results presented in \cite{kopp2018community} in the domain of computer networks the overlap of clusters is not great. +However as experimental results presented in \todo{citation Kopp paper} in the domain of computer networks the overlap of clusters is not great. It, of course, depends on each individual network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. \\ For this thesis, the random selection algorithm serves as a baseline in comparing the results to other methods. The number of samples selected is decided by the number of the full Delta-Medoids algorithm. -\subsection{Greedy Selection} %explanation of my method +\subsection{Greedy Selection}\label{subsec:greedy_select} The greedy selection algorithm is a simple straightforward algorithm that selects a random sample from the set as the first representative. Then it looks for the most dissimilar sample in the rest of the dataset. @@ -533,7 +525,7 @@ \subsection{Greedy Selection} %explanation of my method In the worst case scenario, all points would be selected as representatives while always calculating the distance to each remaining sample in the set. Thus reaching the time complexity of $\mathcal{O}(n^2)$. -\subsection{$\delta$-Medoids} +\subsection{$\delta$-Medoids}\label{subsec:delta_medoids} The two following algorithms were first eplained in \cite{liebman2015representative}. They were tested on two different problems that also use clustering in non-metric space - computing distance of two musical segments and comparing trajectories of objects. As was explained in Chapter 2, the term centroid is not relevant in arbitrary spaces, therefore the term medoid is used. @@ -548,7 +540,7 @@ \subsubsection{$\delta$-Medoids One-Shot} The idea behind this algorithm is to go through the dataset looking for representatives that differ at least by the given parameter $\delta$ from each previously selected representatives. By taking this approach it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. \\ -The algorithm is shown in Figure \todo{label and link}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. +The algorithm is shown in Figure~\ref{alg:delta_medoids_one_shot}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. \begin{algorithm} @@ -579,7 +571,8 @@ \subsubsection{$\delta$-Medoids} The full version of the algorithm runs a one-shot algorithm multiple times. Each time it goes through the dataset it selects a better medoid than before. -Once 2 passes through the data do not change any medoid, the algorithm stops. \\ +Once two passes through the data do not change any medoid, the algorithm stops. +This algorithm is shown in Figure~\ref{alg:delta_medoids_full}.\\ This approach is clearly slower, as the main routine from one shot algorithm has to be run multiple times. \todo{Mention convergence of the algorithm.} @@ -616,47 +609,13 @@ \subsubsection{$\delta$-Medoids} \STATE Initialize $representatives_t = \emptyset$ \FOR{\textit{cluster} in \textit{clusters}} \STATE $representative = \textrm{argmin}_{s \in cluster} (\sum\limits_{x \in cluster}{d(x,s) : d(x,s) \le \delta)}$ - \STATE add \textit{representative} to $representatives_t$ - \ENDFOR - \DOWHILE{$representatives_t = representatives_{t-1}$} - \end{algorithmic} -\end{algorithm} - -%\begin{algorithm} -% \caption{$\delta$-Medoids One-shot PAPER} -% \label{delta_medoids_one_shot} -% \begin{algorithmic}[1] -% \INPUT data $x_0$ ... $x_m$, required distance $\delta$ -% \STATE Initialize \textit{representatives} = $\emptyset$ -% \STATE Initialize \textit{clusters} = $\emptyset$ -% \FOR{$i = 0$ \textbf{to} $m$} -% \STATE Initialize \textit{dist} = $\infty$ -% \STATE Initialize \textit{representative = null} -% \FOR{\textit{rep} in \textit{representatives}} -% \IF{$d(x_i, rep) \le dist$} -% \STATE \textit{representative = rep} -% \STATE $dist = d(x_i, rep)$ -% \ENDIF -% \ENDFOR -% \IF{$dist \le \delta$} -% \STATE add $x_i$ to $cluster_{representative}$ -% \ELSE -% \STATE \textit{representative = $x_i$} -% \STATE Initialize $cluster_{representative} = \emptyset$ -% \STATE ad $x_i$ to $cluster_{representative}$ -% \STATE add $cluster_{representative}$ to \textit{clusters} -% \ENDIF -% \ENDFOR -% \end{algorithmic} -%\end{algorithm} - -\section{Datasets} -The task of cluster representation in non-metric spaces generally is not necessarily connected only to the Security field. -That is why the selected algorithms were tested on both generally know datasets like pendigits and multiple features and on specific data collected from network. -What datasets am I using and why? - -\section{Testbed} - + \STATE add \textit{representative} to $representatives_t$ + \ENDFOR + \DOWHILE{$representatives_t = representatives_{t-1}$} + \end{algorithmic} +\end{algorithm} + +\section{Datasets}\label{sec:datasets} The task of cluster representation in non-metric spaces generally is not necessarily connected only to the Security field. That is why the selected algorithms were tested on artificially created datasets, clustering datasets previously used in literature and on specific data collected from a real network. Datasets that are needed for testing methods from this thesis have to be labeled. @@ -693,17 +652,23 @@ \subsection{Real Network Dataset} \section{Testbed} Testing of each method was done in several steps: Data preparation - creation of pandas dataframes and loading it into special Cluster Class. -Cross-validation of each method. 80\% of samples used for training, 20\% for testing. -Testing was done by kNN k = 1, and creation of confusion matrix while calculating precision and recall. +Cross-validation of each method. 80% of samples used for training, 20% for testing. +Testing was done by $K$-Nearest Neighbor with $K = 1$, and creation of confusion matrix while calculating precision and recall. The testbed application was implemented in Python 3 using the Jupyter notebook technology. It consists of 3 steps: \begin{itemize} \item Loading data into TestDF - \item Training model on 80\% of dataset sample - \item Testing by k-Nearest Neighbor classification method on the remaining 20\% + \item Training model on 80% of dataset sample + \item Testing by k-Nearest Neighbor classification method on the remaining 20% \end{itemize} +\subsection{Loading Data} +Firstly, it is needed to load data from a CSV file and prepare them for further computation. +Pandas library for Python 3 is used for it in implementation. + +%========================================CHAPTER================================== + \chapter{Results} % Testbed explanation - why is it the best etc. @@ -712,10 +677,13 @@ \chapter{Results} \setsecnumdepth{part} + +%========================================CHAPTER================================== + \chapter{Conclusion} Literature in both Network Anomaly Detection field and representative selection was studied. Approaches and algorithms thus found were combined and implemented in prototype program. -Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. \\ +Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. \\ For testing the selected method datasets were created and gathered from the internet to create a diverse testbed for comparing algorithms. From the selected algorithms both $\delta$-Medoids One Shot and $\delta$-Medoids outperformed Random selection of representatives, which is the current solution of representatives selection in the framework. From 57bbec0d6d7af860921c03df0e645a18ea111a2b Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 26 Apr 2019 13:53:05 +0200 Subject: [PATCH 26/65] fixed communication mistakes with VH --- text/hlavac-thesis.tex | 270 +++++++++++++++++++++-------------------- 1 file changed, 136 insertions(+), 134 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index d8bcd87..83a814e 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -35,7 +35,7 @@ \algdef{SE}[DOWHILE]{DO}{DOWHILE}{\algorithmicdo}[1]{\algorithmicwhile\ #1}% %\usepackage[main=english,czech]{babel} - + % % list of acronyms % \usepackage[acronym,nonumberlist,toc,numberedsection=autolabel]{glossaries} % \iflanguage{czech}{\renewcommand*{\acronymname}{Seznam pou{\v z}it{\' y}ch zkratek}}{} @@ -86,13 +86,13 @@ \chapter{Introduction} It can take a long time before the breach is noticed. A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. \\ - + One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). NBAD compares the current behavior of the network to a model created from previous behavior. Devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. Models are calculated from the data collected. If a significant change in behavior is detected, security personnel are notified, and they can act accordingly. \\ - + It is normal for a company to have up to millions of devices. Straight forward usage of NBAD in these big networks is not sufficient to detect breaches successfully. For example, a single infected device that can be easily missed in the traffic of a bigger company. @@ -101,8 +101,8 @@ \chapter{Introduction} Cognitive targeted anomaly detection framework from Cisco serves as an example of such clustering. Each cluster that it creates consists of hosts that behave similarly. -The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ - +The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ + Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ @@ -111,12 +111,12 @@ \chapter{Introduction} %The main focus of this work is in representing these clusters found by clustering algorithms. %Finding representation of clusters in Cognitive targeted anomaly detection framework is made more difficult by the fact, that similarity measure used for clustering does not form a metric space. %Therefore the focus on methods that can be used for selecting representatives in non-metric spaces. \\ -% -%Each cluster can have up to several thousands of hosts. +% +%Each cluster can have up to several thousands of hosts. %Algorithms considered in this thesis were selected based on the following expectations. %As the hosts are clustered based on their behavior, it is safe to expect that selecting a subset of the cluster can represent its behavior without losing significant information. %Furthermore having fewer hosts as representatives of each cluster significantly reduce memory usage. \\ - + %Why use clustering methods in network anomaly detection? %Why do we need representative selection? %What are the next sections about? @@ -128,21 +128,21 @@ \chapter{Introduction} %Clustering is also used for Anomaly Detection in computer systems. \par %In this work I collected 5TODO known methods used in different fields for %cluster representation. What these methods have in common is possibility to use -%them in spaces where only generic similarity measure exists (these spaces do +%them in spaces where only generic similarity measure exists (these spaces do %not necessarily need to be metric). \chapter{Goals} This work aims to study methods used for the representation of network host behavioral clusters that can be used in Cognitive targeted anomaly detection framework. Behavioral similarity used for clustering in this framework does form a metric space, which creates a requirement for algorithms used for representative selection. \\ - + This thesis should present a method that can be used for the representation of clusters in non-metric space. This representation serves as cluster prototypes for determining into which group of network hosts do the newly added hosts belong without saving the information about all hosts in that cluster. \\ Networks monitored by Cognitive targeted anomaly detection framework can have up to several tens of thousands of hosts which leads to massive clusters. If the classification of newly added hosts were done by comparing the new host to all others, the memory and time needed for classification would be unmanageable. -That is why the size of cluster prototypes (number of representatives selected) should be kept as small as possible. \\ - +That is why the size of cluster prototypes (number of representatives selected) should be kept as small as possible. \\ + General methods used for general cluster representation in non-metric spaces should be studied. From these previously studied methods, the best ones will be selected and implemented. As part of this thesis, there should also be created as a benchmark dataset for testing the chosen method. @@ -161,85 +161,85 @@ \chapter{Intrusion Detection System} In Section~\ref{sec:nbad}, the scope is narrowed to network anomaly detection IDS and Section~\ref{sec:challenges}delves deeper into the challenges of it. Section~\ref{sec:ctadf} serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. Methods researched in this thesis were tested on real data collected from Cisco's framework. \\ - -\section{Types of Intrusion Detection Systems}\label{sec:ids} - + +\section{Types of Intrusion Detection Systems}\label{sec:ids} + An intrusion detection system (IDS) is a security tool designed for identification of unauthorized use or abuse of computer systems by both system insiders and external penetrators \cite{mukherjee1994network}. IDS that is designed specifically for monitoring computer network is called Network IDS (NIDS) as in comparison to host IDS and hybrid IDS. Host IDS focuses on monitoring the internal state of a computer system (e. g. mainframe computer) and its dynamic behavior. Hybrid IDS combines different techniques including the ones used in a host and network IDS creating an IDS that can leverage information from each of its parts for better intrusion detection. -Examples of NIDS software are: -\begin{itemize} - \item Snort - \item Suricata - \item Bro Network Security Monitor -\end{itemize} -\todo{citations for each NIDS, website enough?} +Examples of NIDS software are: +\begin{itemize} + \item Snort + \item Suricata + \item Bro Network Security Monitor +\end{itemize} +\todo{citations for each NIDS, website enough?} These systems are used on computer networks to detect and sometimes even prevent attacks that are threats to one of the essential services of such a network. -These basic services are\cite{mukherjee1994network}: -\begin{itemize} - \item Data confidentiality - \item Data and communication integrity - \item Accessibility -\end{itemize} +These basic services are\cite{mukherjee1994network}: +\begin{itemize} + \item Data confidentiality + \item Data and communication integrity + \item Accessibility +\end{itemize} Attackers try to disrupt these services by accessing confidential information (snooping), manipulating information (data tampering attacks) or disabling access to network services (Denial of Service attacks). IDS must have multiple components to detect as many of these attacks as possible. As each attack kind of intrusion is better detected by a different method, there are several types of IDS \cite{liao2013intrusion}: -\begin{itemize} +\begin{itemize} \item \textbf{Signature-based} (knowledge-based): Patterns of known attacks or threats are being compared to captured events for intrusion detection. - \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time and current network behavior is compared to expected (model) behavior for anomalies. - \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. -\end{itemize} -Cognitive targeted anomaly detection framework combines all of these approaches. -This thesis focuses on the behavior-based part of the framework. - -\section{Network Anomaly Detection}\label{sec:nbad} + \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time and current network behavior is compared to expected (model) behavior for anomalies. + \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. +\end{itemize} +Cognitive targeted anomaly detection framework combines all of these approaches. +This thesis focuses on the behavior-based part of the framework. + +\section{Network Anomaly Detection}\label{sec:nbad} Network anomaly detection is a method used in Intrusion Detection Systems (IDS) as explained in the previous section. -This method focuses on comparing network host behavior changes in time. +This method focuses on comparing network host behavior changes in time. If a change greater than a certain threshold is observed, network anomaly is detected. In IDS, when an anomaly is detected, an alert is created to inform the network administrator about what has happened. This alert can be any kind of message ranging from a syslog message to an email sent to the admin. \\ - -Host behavior is collected from devices in the network. + +Host behavior is collected from devices in the network. There is at least one (although many times multiple) device set up for collecting network flows (using NetFlow protocol). Flow collection is the most widely used method for gathering data on the network. One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and size of data transferred in each direction. -\todo{[RFC2722] citation} +\todo{[RFC2722] citation} This aggregation of information enables much faster (even real-time) detection of problems on a network as compared to, for example, deep packet inspection (DPI). -DPI is another method used to detect problems in a network. +DPI is another method used to detect problems in a network. It focuses on exploring the payload of each packet and is mostly considered unusable. Not only the majority of traffic is encrypted, but it is also impossible to look into each packet because of the enormous amount of traffic in today's networks. \\ - + Network flows are collected and then sent via NetFlow protocol to one place where they are stored, and evaluated by detection algorithms. Network anomaly detection system creates a baseline model of the network behavior, which is then compared to the actual traffic. -Any sudden change is considered to be an anomaly that is reported by the IDS. \\ - -\section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} +Any sudden change is considered to be an anomaly that is reported by the IDS. \\ + +\section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} Even if it were possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100\% precision. Not every network host generates enough traffic to create a precise model of its behavior. Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. \\ -\todo{Explain better, what is the goal of anomaly detection, Grill Thesis} - +\todo{Explain better, what is the goal of anomaly detection, Grill Thesis} + That is why models are calculated for whole networks where it is possible to create a model that reflects the behavior of the network. This method is widely used, and on smaller (several devices) networks it is very efficient. However, capturing flow data from a big network of thousands of devices can result in too much information. One can easily miss one infected device generating different traffic. -The noise in the collected data is too high. \\ - +The noise in the collected data is too high. \\ + To give an example, imagine detection of a single infected device, such as a printer in a 10 story office building. This printer, being a part of a botnet, was ordered to generate traffic for DDoS (Distributed Denial of Service) attacks. Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personnel working in the building. \\ - + /todo{MK - WrongPlacement. I want to list a few methods different from ours to tackle the same problem.} -A way to approach this problem is explained in \cite{xu2011network}. +A way to approach this problem is explained in \cite{xu2011network}. The model is created by creating bi-partite graphs for communication of hosts in one network prefix. These graphs are created by looking at hosts from the perspective of whom does it communicate with. -From these graphs, an adjacency matrix is created. +From these graphs, an adjacency matrix is created. How adjacent one host is to another is calculated as a normalized weight of edges in the graph. -This adjacency matrix then serves as a baseline model for anomaly detection. +This adjacency matrix then serves as a baseline model for anomaly detection. Assessing each time window of traffic means calculating an adjacency matrix for this time window and then comparing to the trained model. -If there is a substantial difference, an anomaly is detected. +If there is a substantial difference, an anomaly is detected. This method is proven to work; however, it suffers from the noise of too many hosts explained before. Also, it might not be a good way to look at the traffic from the perspective of a network prefix, as many times a lot of different devices are on one prefix. \\ \todo{Other ways of detection} @@ -257,27 +257,27 @@ \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} In conclusion, a good anomaly detection system should detect all anomalies that are somehow connected to malicious behavior while trying to keep the number of false positives alerts as small as possible. The small number of false positives that are reported is then double-checked in the following layers in the IDS. -\section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} - +\section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} + Algorithms studied in this thesis are tested as a part of Cognitive Targeted Anomaly Detection Framework which is a part of Cognitive Threat Analytics developed and used by Cisco. This framework successfully uses community-based clustering on the behavior of each host. The aim is to split the whole network into smaller groups. Running anomaly detection for each group then yields significantly better results as compared to the traditional whole network approach. Not only it works better because of community-based clustering, but also because of the ability to adapt dynamically as the network changes. Thus it is able to incorporate changes that happen on a given network such as adding and removing devices.\\ - -The method is separated into two phases. + +The method is separated into two phases. The initial phase is used to learn the first state of the network, and the second ongoing phase starts when the initial model is created. During this ongoing phase, the framework can dynamically adjust clusters to the current state of the network. \\ - -The initial phase starts by collecting 24 hours of traffic from a given network. -Collected data consists of network traffic flows and proxy server logs. -Once the 24-hour period is over, clustering of hosts starts. -Each host is represented by a tuple $h = {S^h, F^h}$, where: -\begin{itemize} - \item $S^h$ represents set of all visited pairs server:port - \item $F^h$ represents the frequency of visits of server:port pairs -\end{itemize} + +The initial phase starts by collecting 24 hours of traffic from a given network. +Collected data consists of network traffic flows and proxy server logs. +Once the 24-hour period is over, clustering of hosts starts. +Each host is represented by a tuple $h = {S^h, F^h}$, where: +\begin{itemize} + \item $S^h$ represents set of all visited pairs server:port + \item $F^h$ represents the frequency of visits of server:port pairs +\end{itemize} This frequency-based approach is chosen so that some servers like Facebook or Google do not overwhelm the statistics in the model. When each host has its representation, the clustering algorithm is started. A technique called community-based clustering is used to create groups of hosts. @@ -287,7 +287,9 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: -$$cos(a, b) = \frac{\sum_{s \in S} F_s^a F_s^b} {\sqrt{\sum_{s \in S} (F_s^a)^2} \sqrt{\sum_{s \in S} (F_s^b)^2}}$$ +\[ +\textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}} +\] With this similarity measure it is possible to determine cluster of hosts with similar behavior. \\ After the clustering is done, only clusters that are bigger than 10 hosts are selected for representative selection. @@ -295,18 +297,18 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} For the purpose of this work, we do not consider clusters smaller than 10 hosts. \\ When clusters are determined, representatives are selected from each cluster. -Currently, $\kappa$ hosts are selected at random for each cluster, where $\kappa$ is selected based on parameters in the whole framework. +Currently, $\kappa$ hosts are selected at random for each cluster, where $\kappa$ is selected based on parameters in the whole framework. In this thesis, tests were made to improve this random selection method. -After that, each host is then given a random number between 1 and 12 which serves as a time to live for that method. +After that, each host is then given a random number between 1 and 12 which serves as a time to live for that method. This time to live is crucial for dynamic changes in the network because clusters can change or even die out if they do not maintain their behavior. This is one of the parameters based on which the number of representatives $\kappa$ is selected. \\ - + After the initial training phase is over and the model is established, data continues to be collected. Every 4 hours all hosts that are observed are assigned to existing clusters. For each cluster, number serving as a time to live is decreased, and hosts that reach 0 are removed from the cluster. Free spaces are filled with hosts selected form hosts that were assigned to the cluster from these 4 hours. -These clusters are then further used for in anomaly detection. +These clusters are then further used for in anomaly detection. They serve as a foundation for calculating baseline behavior for hosts belonging to it. If a host is known to belong to a cluster $A$ and its behavior suddenly starts to differ from the behavior of representatives selected for cluster $A$, an anomaly is found and reported further into the NIDS. @@ -315,89 +317,89 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} \chapter{Theoretical Overview} This chapter serves as a theoretical background to explain clustering methods and the topic of non-metric spaces. - + \section{Introduction to Clustering}\label{sec:clust_intro} As \cite{guttag2016introduction} defines it, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. Without any prior knowledge of the data, this method looks for structures in feature vectors. Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. -Variability is defined as $$ variability(c) = \sum_{e \in c} distance(mean(c), e)^2\:,$$ where \textit{e} is an object from given cluster. +Variability is defined as $$ \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:,$$ where \textit{e} is an object from given cluster. For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. -Dissimilarity is defined as $$ dissimilarity(C) = \sum_{c \in C} variability(c)\:,$$ where C stands for all clusters the data was clustered into. +Dissimilarity is defined as $$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:,$$ where C stands for all clusters the data was clustered into. Given this definition, the best way to cluster every dataset would be to put each object to its cluster. That would not lead to any reasonable result. -Therefore there is a constraint added for clustering methods. +Therefore there is a constraint added for clustering methods. It can be either the maximum number of clusters or the maximum distance between two clusters. \\ - + The distance is a measure that quantifies how far two objects with the same features are. -Many different distance measures are used in clustering. -Several of commonly used ones are: -\begin{itemize} - \item Euclidean distance - \item Minkowski distance - \item Manhattan distance - \item Cosine similarity +Many different distance measures are used in clustering. +Several of commonly used ones are: +\begin{itemize} + \item Euclidean distance + \item Minkowski distance + \item Manhattan distance + \item Cosine similarity \end{itemize} \todo{Add formulas.} -\todo{kulback-leibler add?} - +\todo{kulback-leibler add?} + A straightforward example of clustering is a method called hierarchical clustering. Given $N$ objects in a dataset, it creates $N$ clusters - meaning there is a cluster for each object. -The method looks for two closest clusters and merges them into one. +The method looks for two closest clusters and merges them into one. This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. This method is a greedy algorithm, and therefore it might not result in globally optimal clustering. -Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$. +Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$. Therefore, it cannot be used in big datasets. \\ - + An example of a much faster clustering algorithm that is also greedy is $K$-means. \todo{How to cite that this is only taken from the book, even though rewritten and selected for my purposes.} The '$K$' in $K$-means stands for the number of clusters that we want to get as a result. To use this algorithm the number of desired clusters has to be known in advance. $K$-means randomly chooses $K$ centroids in the space of the dataset and then assigns each point to a centroid. After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. -The algorithm stops when the centroids of clusters stop changing. -Figure~\ref{alg:kmeans} shows the pseudocode of $K$-means. -\begin{algorithm}\label{alg:kmeans} - \caption{k-Means pseudocode} - \label{k_mean_pseudocode} - \begin{algorithmic}[1] - \INPUT set of examples $\alpha$ number of clusters $k$ - \STATE Randomly chose $k$ examples from $\alpha$ - \WHILE True +The algorithm stops when the centroids of clusters stop changing. +Figure~\ref{alg:kmeans} shows the pseudocode of $K$-means. +\begin{algorithm}\label{alg:kmeans} + \caption{k-Means pseudocode} + \label{k_mean_pseudocode} + \begin{algorithmic}[1] + \INPUT set of examples $\alpha$ number of clusters $k$ + \STATE Randomly chose $k$ examples from $\alpha$ + \WHILE True \STATE Create $k$ clusters by assigning each object to the closest centroid - \STATE Compute $k$ new centroids by averaging objects in each cluster - \IF{Centroids do not change} - \STATE \textbf{break} - \ENDIF - \ENDWHILE - \end{algorithmic} -\end{algorithm} - + \STATE Compute $k$ new centroids by averaging objects in each cluster + \IF{Centroids do not change} + \STATE \textbf{break} + \ENDIF + \ENDWHILE + \end{algorithmic} +\end{algorithm} + This algorithm is fast, it has time complexity $\mathcal{O}(k*n*d)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. It is the most commonly used clustering algorithm as it in most cases converges in a few iterations. Some of the ideas from it are used in the delta medoids algorithm that is used for clustering in this thesis. \\ - -%summary + +%summary The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. Firstly the number of clusters is not previously known, so choosing $K$ for $K$-means is not an option, and secondly, the distance measure used does not form a metric space. Next section delves deeper into what it means when a measure does not form a metric space. - + \section{Clustering on Topological Space}\label{sec:cluster_topo} A metric space is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X$ into $R$ which satisfies the following conditions: -\begin{enumerate} - \item [(i)] $d(x, y) \geq 0$; - \item [(ii)] $d(x, y) = 0 \iff x = y$; - \item [(iii)] $d(x, y) = d(y, x)$ - \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. -\end{enumerate} - +\begin{enumerate} + \item [(i)] $d(x, y) \geq 0$; + \item [(ii)] $d(x, y) = 0 \iff x = y$; + \item [(iii)] $d(x, y) = d(y, x)$ + \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. +\end{enumerate} + The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above and therefore, it does not form a metric space but a more general topological space. This is how \cite{stahl2014introduction} defines it. \\ Given any set $S$ a topology on S is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$, and with the following properties: -\begin{enumerate} - \item [(i)] The empty set $\emptyset$ is in $F$. - \item [(ii)] The given set $S$ is in $F$. +\begin{enumerate} + \item [(i)] The empty set $\emptyset$ is in $F$. + \item [(ii)] The given set $S$ is in $F$. \item [(iii)] The intersection of any two sets of $F$ is in $F$. \item [(iv)] The union of any number of sets of $F$ is in $F$. -\end{enumerate} +\end{enumerate} The pair ($S, F$) is called a topological space. \\ \todo{explain about neighborhoods} @@ -472,7 +474,7 @@ \section{Definition of Representative Selection}\label{sec:def_rep_selection} \section{Representative Selection in a Metric Space}\label{sec:rep_select_metric} Representative selection in metric space relies heavily on being able to create centroids. A centroid is a point in space that is not necessarily part of the cluster but sits in the center of the cluster. -A centroid is found by averaging all points of given cluster thus finding a +A centroid is found by averaging all points of given cluster thus finding a % popsani jak by se to delalo normalne % stezejni myslenky proc to delame takto @@ -490,10 +492,10 @@ \chapter{Implementation and Performance Testing of Relevant Methods} The following section serves as a list of used datasets ranging from general to more specific ones. The last section of this chapter talks about the tests that were run and explains why they were chosen. -The programming language chosen for implementation was Python 3.7. +The programming language chosen for implementation was Python 3.7. It allowed different libraries for data manipulation like Pandas library and also is an excellent choice for testing a prototype as the implementation of different algorithms is not complicated. - -\section{Methods Relevant to Cognitive Anomaly Detection}\label{sec:relevant_methods} + +\section{Methods Relevant to Cognitive Anomaly Detection}\label{sec:relevant_methods} Based on the previously explained approaches for finding representatives of clusters in non-metric, spaces the following methods were chosen, tested and compared as a part of this thesis. \begin{itemize} \item Random selection @@ -502,8 +504,8 @@ \section{Methods Relevant to Cognitive Anomaly Detection}\label{sec:relevant_met \item Delta-Medoids \todo{citations} \end{itemize} -\subsection{Random Selection}\label{subsec:random_select} - +\subsection{Random Selection}\label{subsec:random_select} + This is the algorithm that serves as a baseline to measure the improvement as it is the method currently used in Cognitive targeted anomaly detection framework. It simply selects a given number of representatives from the cluster randomly. \\ @@ -609,13 +611,13 @@ \subsubsection{$\delta$-Medoids} \STATE Initialize $representatives_t = \emptyset$ \FOR{\textit{cluster} in \textit{clusters}} \STATE $representative = \textrm{argmin}_{s \in cluster} (\sum\limits_{x \in cluster}{d(x,s) : d(x,s) \le \delta)}$ - \STATE add \textit{representative} to $representatives_t$ - \ENDFOR - \DOWHILE{$representatives_t = representatives_{t-1}$} - \end{algorithmic} -\end{algorithm} - -\section{Datasets}\label{sec:datasets} + \STATE add \textit{representative} to $representatives_t$ + \ENDFOR + \DOWHILE{$representatives_t = representatives_{t-1}$} + \end{algorithmic} +\end{algorithm} + +\section{Datasets}\label{sec:datasets} The task of cluster representation in non-metric spaces generally is not necessarily connected only to the Security field. That is why the selected algorithms were tested on artificially created datasets, clustering datasets previously used in literature and on specific data collected from a real network. Datasets that are needed for testing methods from this thesis have to be labeled. @@ -683,7 +685,7 @@ \chapter{Results} \chapter{Conclusion} Literature in both Network Anomaly Detection field and representative selection was studied. Approaches and algorithms thus found were combined and implemented in prototype program. -Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. \\ +Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. \\ For testing the selected method datasets were created and gathered from the internet to create a diverse testbed for comparing algorithms. From the selected algorithms both $\delta$-Medoids One Shot and $\delta$-Medoids outperformed Random selection of representatives, which is the current solution of representatives selection in the framework. From 4fb4717ad2541f8f13b6e8b7e158cef749b2d0a5 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Mon, 6 May 2019 17:28:36 +0200 Subject: [PATCH 27/65] final chapters division, 0.3 MK comments added --- text/hlavac-thesis.bib | 22 ++ text/hlavac-thesis.tex | 532 ++++++++++++++++++++++++++--------------- 2 files changed, 361 insertions(+), 193 deletions(-) diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib index 78c6911..0affa90 100644 --- a/text/hlavac-thesis.bib +++ b/text/hlavac-thesis.bib @@ -57,10 +57,32 @@ @book{guttag2016introduction year={2016}, publisher={MIT Press} } +@book{choudhary1992elements, + title={The elements of complex analysis}, + author={Choudhary, B}, + year={1992}, + publisher={New Age International}, + pages={20--20}, + ISBN={978-81-224-0399} +} @book{stahl2014introduction, title={Introduction to topology and geometry}, author={Stahl, Saul and Stenson, Catherine}, year={2014}, publisher={John Wiley \& Sons} } +@online{cooper2019bestids, + author={Cooper, Stephen}, + title={2019 Best Intrusion Detection Systems (10+ IDS Tools Reviewed)}, + year={2019}, + url={https://www.comparitech.com/net-admin/network-intrusion-detection-tools/}, + urldate={2019-04-27} +} +@misc{dua1998pendigit, +author = "Dua, Dheeru and Graff, Casey", +year = "2017", +title = "{UCI} Machine Learning Repository", +url = "http://archive.ics.uci.edu/ml", +institution = "University of California, Irvine, School of Information and Computer Sciences" +} diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 83a814e..0728992 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -25,7 +25,7 @@ \usepackage{color} %VH for comments \usepackage{csquotes} -\usepackage{todonotes} %for TODO notes - can be removed in final +\usepackage{todonotes} \usepackage{amssymb} @@ -73,40 +73,41 @@ \setsecnumdepth{part} \chapter{Introduction} -Even several decades after the Internet became the most used medium for communication, there are still many issues concerning its security. -Businesses all around the world daily connect more of their infrastructure to the Internet thus creating a possibility for attackers to exploit the connected devices for their malicious use. -Security engineers are always inventing new methods to stop the attacks and secure their networks. -Attackers race to overcome these newly created methods and to gain control of protected data or devices. -This race creates a need for much more sophisticated defense mechanisms. \\ +There are still many security issues on the Internet even several decades after it became the dominant medium for communication. +Companies all around the world connect more of their infrastructure to the Internet. +Each connected device is an opportunity for attackers. +Security engineers are always inventing new methods to stop the attacks and keep their networks safe and secure. +It is a never-ending race between attackers, trying to overcome new security methods, and defenders, the security personnel, trying to be always one step ahead of the attackers. +This race creates the drive for more and more sophisticated defense mechanisms. \\ Multi-layer defense systems are protecting contemporary networks. -As attackers overcome one layer, there is another layer waiting for them after it. -However, if the attackers succeed to bypass all of them unnoticed a huge problem can occur. +As attackers overcome one layer, there is yet another layer waiting for them. +However, if the attackers succeed to bypass all of them unnoticed, a huge problem can occur. They can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. -It can take a long time before the breach is noticed. +It can take a long time before somebody notices the breach. A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. \\ One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). -NBAD compares the current behavior of the network to a model created from previous behavior. -Devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. -Models are calculated from the data collected. -If a significant change in behavior is detected, security personnel are notified, and they can act accordingly. \\ +In NBAD, devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. +The method then compares the current behavior of the network to a model created from the historical data. +If a significant deviation in behavior occurs, the security team is notified and can act accordingly. \\ -It is normal for a company to have up to millions of devices. -Straight forward usage of NBAD in these big networks is not sufficient to detect breaches successfully. -For example, a single infected device that can be easily missed in the traffic of a bigger company. -In order to detect such anomalies in this network, it is needed to focus on smaller parts of it. -Clustering based on the behavior of network hosts can be used to separate the network into smaller pieces. \\ +It is not uncommon for a company to have tens of thousands of devices and even more. +Straight forward usage of NBAD in these networks is not sufficient to detect breaches successfully. +For example, infection of a single device can be missed in the traffic of a medium sized company. +In order to detect such anomalies, it is needed to focus on smaller parts of the network. +Behavioral clustering of network hosts can be used to separate it into smaller pieces. \\ -Cognitive targeted anomaly detection framework from Cisco serves as an example of such clustering. -Each cluster that it creates consists of hosts that behave similarly. -The main focus of this work is finding a representation of these clusters that can be used to calculate the model behavior and also be dynamically updated as the behavior of the network changes. \\ +In Cognitive targeted anomaly detection framework from Cisco behavioral clustering is used to find groups of similar hosts in a network. +Each cluster created consists of hosts that have similar behavior. +The main focus of this work is to find a representation of these clusters that are used to calculate the model behavior. +These cluster prototypes\todo{Can I use the term cluster prototype before first explaining it?} are then updated dynamically as the behavior of the network changes. \\ Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ -(Chapters and sections will be briefly explained here.) +\todo{Chapters and sections will be briefly explained here.} %The main focus of this work is in representing these clusters found by clustering algorithms. %Finding representation of clusters in Cognitive targeted anomaly detection framework is made more difficult by the fact, that similarity measure used for clustering does not form a metric space. @@ -134,21 +135,22 @@ \chapter{Introduction} \chapter{Goals} This work aims to study methods used for the representation of network host behavioral clusters that can be used in Cognitive targeted anomaly detection framework. -Behavioral similarity used for clustering in this framework does form a metric space, which creates a requirement for algorithms used for representative selection. \\ +Behavioral similarity used for clustering in this framework does not form a metric space, which creates a requirement for algorithms used for representative selection. \\ -This thesis should present a method that can be used for the representation of clusters in non-metric space. -This representation serves as cluster prototypes for determining into which group of network hosts do the newly added hosts belong without saving the information about all hosts in that cluster. \\ +This thesis should present a method that can be used for finding the cluster prototypes of clusters in non-metric space. +These cluster prototypes represent the whole cluster in a way that is convenient for further computation. +It can be, for example, assigning newly added samples to their corresponding cluster.\\ -Networks monitored by Cognitive targeted anomaly detection framework can have up to several tens of thousands of hosts which leads to massive clusters. +Networks monitored by Cognitive targeted anomaly detection framework can have up to several hundreds of thousands of hosts which leads to massive clusters. If the classification of newly added hosts were done by comparing the new host to all others, the memory and time needed for classification would be unmanageable. -That is why the size of cluster prototypes (number of representatives selected) should be kept as small as possible. \\ +That is why the number of representatives selected from each cluster should be kept as small as possible. \\ General methods used for general cluster representation in non-metric spaces should be studied. From these previously studied methods, the best ones will be selected and implemented. -As part of this thesis, there should also be created as a benchmark dataset for testing the chosen method. +As part of this thesis, there should also be created a dataset as a benchmark for testing the chosen method. A further goal is to test the chosen methods on generally used clustering datasets and the created benchmark dataset. Based on this testing, find the method that suits best for use in this field and incorporates it into the Cognitive targeted anomaly detection framework. -The selected method should be finetuned to get the best possible results in anomaly-detection. \todo{Would this paragraph be more clear if I made a bullet point list?} +The selected method should be finetuned to get the best possible results in anomaly-detection. \setsecnumdepth{all} @@ -160,21 +162,20 @@ \chapter{Intrusion Detection System} Different approaches to network intrusion detection systems (IDS) are explained in Section~\ref{sec:ids}. In Section~\ref{sec:nbad}, the scope is narrowed to network anomaly detection IDS and Section~\ref{sec:challenges}delves deeper into the challenges of it. Section~\ref{sec:ctadf} serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. -Methods researched in this thesis were tested on real data collected from Cisco's framework. \\ +Methods researched in this thesis were tested on real data collected from Cognitive targeted anomaly detection framework. \\ \section{Types of Intrusion Detection Systems}\label{sec:ids} An intrusion detection system (IDS) is a security tool designed for identification of unauthorized use or abuse of computer systems by both system insiders and external penetrators \cite{mukherjee1994network}. -IDS that is designed specifically for monitoring computer network is called Network IDS (NIDS) as in comparison to host IDS and hybrid IDS. +IDS that is explicitly designed for monitoring computer network is called Network IDS (NIDS) as in comparison to host IDS and hybrid IDS. Host IDS focuses on monitoring the internal state of a computer system (e. g. mainframe computer) and its dynamic behavior. Hybrid IDS combines different techniques including the ones used in a host and network IDS creating an IDS that can leverage information from each of its parts for better intrusion detection. -Examples of NIDS software are: +Examples of NIDS software are \cite{cooper2019bestids} : \begin{itemize} \item Snort \item Suricata \item Bro Network Security Monitor \end{itemize} -\todo{citations for each NIDS, website enough?} These systems are used on computer networks to detect and sometimes even prevent attacks that are threats to one of the essential services of such a network. These basic services are\cite{mukherjee1994network}: \begin{itemize} @@ -184,10 +185,10 @@ \section{Types of Intrusion Detection Systems}\label{sec:ids} \end{itemize} Attackers try to disrupt these services by accessing confidential information (snooping), manipulating information (data tampering attacks) or disabling access to network services (Denial of Service attacks). IDS must have multiple components to detect as many of these attacks as possible. -As each attack kind of intrusion is better detected by a different method, there are several types of IDS \cite{liao2013intrusion}: +As each kind of intrusion is better detected by a different method, there are several types of IDS \cite{liao2013intrusion}: \begin{itemize} \item \textbf{Signature-based} (knowledge-based): Patterns of known attacks or threats are being compared to captured events for intrusion detection. - \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time and current network behavior is compared to expected (model) behavior for anomalies. + \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time, and current network behavior is compared to expected (model) behavior for anomalies. \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. \end{itemize} Cognitive targeted anomaly detection framework combines all of these approaches. @@ -196,12 +197,12 @@ \section{Types of Intrusion Detection Systems}\label{sec:ids} \section{Network Anomaly Detection}\label{sec:nbad} Network anomaly detection is a method used in Intrusion Detection Systems (IDS) as explained in the previous section. This method focuses on comparing network host behavior changes in time. -If a change greater than a certain threshold is observed, network anomaly is detected. +If a change more significant than a certain threshold is observed, network anomaly is detected. In IDS, when an anomaly is detected, an alert is created to inform the network administrator about what has happened. This alert can be any kind of message ranging from a syslog message to an email sent to the admin. \\ Host behavior is collected from devices in the network. -There is at least one (although many times multiple) device set up for collecting network flows (using NetFlow protocol). +There is at least one device (although many times multiple) dedicated for collection of network flows (using NetFlow protocol). Flow collection is the most widely used method for gathering data on the network. One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and size of data transferred in each direction. \todo{[RFC2722] citation} @@ -215,7 +216,7 @@ \section{Network Anomaly Detection}\label{sec:nbad} Any sudden change is considered to be an anomaly that is reported by the IDS. \\ \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} -Even if it were possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100\% precision. +Even if it was possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100\% precision. Not every network host generates enough traffic to create a precise model of its behavior. Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. \\ \todo{Explain better, what is the goal of anomaly detection, Grill Thesis} @@ -229,30 +230,20 @@ \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} To give an example, imagine detection of a single infected device, such as a printer in a 10 story office building. This printer, being a part of a botnet, was ordered to generate traffic for DDoS (Distributed Denial of Service) attacks. Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. -If considering the traffic of the whole office building, an anomaly detection system could hardly notice a change in all the other legitimate traffic of personnel working in the building. \\ - -/todo{MK - WrongPlacement. I want to list a few methods different from ours to tackle the same problem.} -A way to approach this problem is explained in \cite{xu2011network}. -The model is created by creating bi-partite graphs for communication of hosts in one network prefix. -These graphs are created by looking at hosts from the perspective of whom does it communicate with. -From these graphs, an adjacency matrix is created. -How adjacent one host is to another is calculated as a normalized weight of edges in the graph. -This adjacency matrix then serves as a baseline model for anomaly detection. -Assessing each time window of traffic means calculating an adjacency matrix for this time window and then comparing to the trained model. -If there is a substantial difference, an anomaly is detected. -This method is proven to work; however, it suffers from the noise of too many hosts explained before. -Also, it might not be a good way to look at the traffic from the perspective of a network prefix, as many times a lot of different devices are on one prefix. \\ -\todo{Other ways of detection} +But an anomaly detection system could hardly notice a change when considering the traffic of the whole office building. \\ If an AD system does not mark malicious traffic as an anomaly, it is considered a false negative. -That is another challenge AD systems are struggling with, to keep the number of missed attacks as close to 0 as possible. \\ +That is another challenge AD systems are struggling with, to keep the number of missed attacks at 0 or as close to it as possible.\\ One more problem connected to false negatives are the false positives. A false positive is a network sample of benign network traffic marked as anomalous by the AD system. For example, if we would have our anomaly detection system set up wrong, it might detect an anomaly every day at 8 AM when workers come to the office, start their computers and download daily email. Comparing the time window from before 8 AM and after 8 AM would not give us any relevant information. -This problem can be mitigated by setting a window for which the model is calculated long enough so that these mistakes do not happen. -In other situations, false positive alerts can happen, and the goal of network anomaly detection system is to keep them as close to 0 as possible. \\ +This problem can be mitigated by setting a window, for which the model is calculated, long enough so that these mistakes do not happen. +There are many other situations when false positive alerts can happen. +Having more than 0 false positives is not that big of a problem as having more than 0 false negatives. +In IDS there is always a way to filter and double check the anomalies. +However, a network anomaly detection system should avoid a big overhead in false positives as it could overwhelm the system.\\ In conclusion, a good anomaly detection system should detect all anomalies that are somehow connected to malicious behavior while trying to keep the number of false positives alerts as small as possible. The small number of false positives that are reported is then double-checked in the following layers in the IDS. @@ -260,15 +251,15 @@ \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} Algorithms studied in this thesis are tested as a part of Cognitive Targeted Anomaly Detection Framework which is a part of Cognitive Threat Analytics developed and used by Cisco. -This framework successfully uses community-based clustering on the behavior of each host. +This framework successfully uses community-based clustering on the behavior of each host \cite{kopp2018community}. The aim is to split the whole network into smaller groups. Running anomaly detection for each group then yields significantly better results as compared to the traditional whole network approach. Not only it works better because of community-based clustering, but also because of the ability to adapt dynamically as the network changes. Thus it is able to incorporate changes that happen on a given network such as adding and removing devices.\\ The method is separated into two phases. -The initial phase is used to learn the first state of the network, and the second ongoing phase starts when the initial model is created. -During this ongoing phase, the framework can dynamically adjust clusters to the current state of the network. \\ +In the initial phase, the state of the network is learned, and an initial model is created. +Then, in the second ongoing phase, the framework dynamically adjusts clusters to the current state of the network. \\ The initial phase starts by collecting 24 hours of traffic from a given network. Collected data consists of network traffic flows and proxy server logs. @@ -278,37 +269,35 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} \item $S^h$ represents set of all visited pairs server:port \item $F^h$ represents the frequency of visits of server:port pairs \end{itemize} -This frequency-based approach is chosen so that some servers like Facebook or Google do not overwhelm the statistics in the model. -When each host has its representation, the clustering algorithm is started. +The frequency is defined as: +$$F_s^h = \frac{i}{n}\sum\limits_{1}{n}I(t_i, s, h)\:,$$ where $n$ is the number of time windows, $I$ is the indicator function, which is 1 if the network host $h$ visited the server $s$ in the timewindow $t_i$ and 0 otherwise. +This ratio-based frequency ensures that frequently visited servers (e.g., Google, Facebook) do not overshadow the less frequently visited servers.\\ + +When each host is represented, the clustering algorithm is started. A technique called community-based clustering is used to create groups of hosts. This clustering works for graphs, where communities are more densely connected parts of the graph. In the words of this NBAD: Those hosts that communicate with similar peers are considered to be in the same community. -Community-based clustering is explained in detail in section 2.3. \todo{link to section}. \\ +Community-based clustering is explained in detail in Section~\ref{sec:cluster_topo}.\\ The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: -\[ -\textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}} -\] -With this similarity measure it is possible to determine cluster of hosts with similar behavior. \\ +$$\textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:,$$ +where $F_a$, $F_b$ represent the frequency and $S$ represents the union of sets of servers visited by the network hosts $a$ and $b$. \\ After the clustering is done, only clusters that are bigger than 10 hosts are selected for representative selection. -Smaller clusters are analyzed using a fallback method - host-based anomaly detection. +Smaller clusters are analyzed using a fallback method - host-centered anomaly detection. For the purpose of this work, we do not consider clusters smaller than 10 hosts. \\ -When clusters are determined, representatives are selected from each cluster. -Currently, $\kappa$ hosts are selected at random for each cluster, where $\kappa$ is selected based on parameters in the whole framework. +When clusters are determined, $\kappa$ random representatives are selected from each cluster. +Currently, $\kappa$ is an empirically set parameter. In this thesis, tests were made to improve this random selection method. -After that, each host is then given a random number between 1 and 12 which serves as a time to live for that method. -This time to live is crucial for dynamic changes in the network because clusters can change or even die out if they do not maintain their behavior. -This is one of the parameters based on which the number of representatives $\kappa$ is selected. \\ +A more detailed explanation of this method can be found in \cite{kopp2018community}.\\ After the initial training phase is over and the model is established, data continues to be collected. Every 4 hours all hosts that are observed are assigned to existing clusters. -For each cluster, number serving as a time to live is decreased, and hosts that reach 0 are removed from the cluster. -Free spaces are filled with hosts selected form hosts that were assigned to the cluster from these 4 hours. +At that point, a portion of cluster representatives is replaced by new ones to capture the ever-changing nature of the network data. \\ -These clusters are then further used for in anomaly detection. +These clusters are then further used in anomaly detection. They serve as a foundation for calculating baseline behavior for hosts belonging to it. If a host is known to belong to a cluster $A$ and its behavior suddenly starts to differ from the behavior of representatives selected for cluster $A$, an anomaly is found and reported further into the NIDS. @@ -316,21 +305,129 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} \chapter{Theoretical Overview} -This chapter serves as a theoretical background to explain clustering methods and the topic of non-metric spaces. +This chapter introduces a theoretical background to explain clustering methods, the topic of non-metric spaces and the problem of representative selection. \section{Introduction to Clustering}\label{sec:clust_intro} -As \cite{guttag2016introduction} defines it, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. +As defined in \cite{guttag2016introduction}, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. Without any prior knowledge of the data, this method looks for structures in feature vectors. Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. -Variability is defined as $$ \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:,$$ where \textit{e} is an object from given cluster. +Variability and dissimilarity are two properties defined for a better understanding of the data. \\ +Variability is defined as $$ \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:,$$\todo{number equations} where \textit{e} is an object from given cluster. +The distance is a measure that quantifies how far two objects with the same features are. \todo{better wording here} +Many different distance measures are used in clustering. +Several of commonly used ones are: +\begin{itemize} + \item Euclidean distance + \item Minkowski distance + \item Manhattan distance + \item Cosine similarity +\end{itemize} + +Dissimilarity is defined as $$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:,$$ where $C$ stands for all clusters the data was clustered into. For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. -Dissimilarity is defined as $$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:,$$ where C stands for all clusters the data was clustered into. Given this definition, the best way to cluster every dataset would be to put each object to its cluster. That would not lead to any reasonable result. Therefore there is a constraint added for clustering methods. It can be either the maximum number of clusters or the maximum distance between two clusters. \\ -The distance is a measure that quantifies how far two objects with the same features are. +A straightforward example of clustering is a method called agglomerative hierarchical clustering. +Given $N$ objects in a dataset, it creates $N$ clusters - meaning there is a cluster for each object. +The method looks for two closest clusters and merges them into one. +This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. +This method is a greedy algorithm, and therefore it might not result in globally optimal clustering. +Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$. +Therefore, it cannot be used in big datasets. \\ + +An example of a much faster clustering algorithm that is also greedy is $K$-means. \todo{How to cite that this is only taken from the book, even though rewritten and selected for my purposes.} +The '$K$' in $K$-means stands for the number of clusters that we want to get as a result. +To use this algorithm the number of desired clusters has to be known in advance. +$K$-means randomly chooses $K$ centroids in the space of the dataset and then assigns each point to a centroid. +After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. +The algorithm stops when the centroids of clusters stop changing. +Figure~\ref{alg:kmeans} shows the pseudocode of $K$-means. +\begin{algorithm}\label{alg:kmeans} + \caption{K-Means pseudocode} + \label{k_mean_pseudocode} + \begin{algorithmic}[1] + \INPUT set of examples $X$; number of clusters $k$ + \STATE Randomly chose $k$ examples from $X$ as centroids + \WHILE{True} + \STATE Create $k$ clusters by assigning each object to the closest centroid + \STATE Compute $k$ new centroids by averaging objects in each cluster + \IF{Centroids do not change} + \STATE \textbf{break} + \ENDIF + \ENDWHILE + \end{algorithmic} +\end{algorithm} \todo{better pseudocode, find better book} + +This algorithm is fast, it has time complexity $\mathcal{O}(k \cdot n)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. +It is the most common clustering algorithm as it typically converges in a few iterations. +For more details about basic clustering algorithms see \cite{guttag2016introduction}.\\ + +%summary +The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. +Firstly the number of clusters is not previously known, so choosing $K$ for $K$-means is not an option, and secondly, the distance measure used does not form a metric space. +Next section delves deeper into what it means when a measure does not form a metric space. + +\section{Clustering on Topological Space}\label{sec:cluster_topo} +This section explains the difference between metric and topological space and clustering approaches for topological spaces. \\ + +A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X \to \mathbb{R}$\todo{ad pripominka, ze by to melo byt NxN, takhle to je popsane v te citovane knize. Prijde mi, ze je to obecnejsi a drzim se toho jak to popsal ten matematik.} which satisfies the following conditions: +\begin{enumerate} + \item [(i)] $d(x, y) \geq 0$; + \item [(ii)] $d(x, y) = 0 \iff x = y$; + \item [(iii)] $d(x, y) = d(y, x)$ + \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \mathrm{for} x, y, z \in X$. +\end{enumerate} + +The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. +This is why this similarity measure forms a topological space, which is defined according to, e.g. \cite{stahl2014introduction}. \\ + +Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: +\begin{enumerate} + \item [(i)] The empty set $\emptyset$ is in $F$. + \item [(ii)] The given set $S$ is in $F$. + \item [(iii)] The intersection of any two sets of $F$ is in $F$. + \item [(iv)] The union of any number of sets of $F$ is in $F$. +\end{enumerate} +The ordered pair ($S, F$) is called a topological space. \\ + +\todo{explain about neighborhoods} + +K-Means clustering from the previous section cannot be used in topological space as the centroids do not exist. +In many cases, there is no prior knowledge of k, as is the case in this thesis. +A popular method that is used for clustering in non-metric spaces is called community-based clustering. \\ + +Community-based clustering detects communities in the data. +A community is a subset of examples in the data, that is densely connected with each other. +One of the ways how to detect communities in a graph is to create a full-adjacency matrix. +This matrix contains all connections between all nodes in the given graph. +Analyzing the matrix then can tell us about the densities in different parts of graphs.\\ + +Louvain method is a similar approach to clustering when we do not have a simple graph but a set of samples and a pairwise similarity measure. +This method relies on creating a full similarity matrix for the whole dataset and then looking for communities in the data. \\ + +In Cognitive Targeted Anomaly Detection Framework Louvain method cannot be used directly as calculating the full similarity matrix has a complexity of $\mathcal(n^2)$, which is impossible to calculate for a large network. +That is why an approximative clustering method is used. +This method iteratively samples network hosts and runs the clustering algorithm on the sampled hosts. +Each iteration creates or updates cluster prototypes. +If the data in the current batch fit into a previously prototyped cluster, they are added to it and the cluster prototype is updated. +If samples differ more than a predefined threshold a new cluster prototype is created. + +%========================================CHAPTER================================== + +\chapter{Representative Selection} + +This chapter introduces a theoretical background to explain clustering methods, the topic of non-metric spaces and the problem of representative selection. + +\section{Introduction to Clustering}\label{sec:clust_intro} +As defined in \cite{guttag2016introduction}, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. +Without any prior knowledge of the data, this method looks for structures in feature vectors. +Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. +Variability and dissimilarity are two properties defined for a better understanding of the data. \\ +Variability is defined as $$ \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:,$$\todo{number equations} where \textit{e} is an object from given cluster. +The distance is a measure that quantifies how far two objects with the same features are. \todo{better wording here} Many different distance measures are used in clustering. Several of commonly used ones are: \begin{itemize} @@ -338,10 +435,16 @@ \section{Introduction to Clustering}\label{sec:clust_intro} \item Minkowski distance \item Manhattan distance \item Cosine similarity -\end{itemize} \todo{Add formulas.} -\todo{kulback-leibler add?} +\end{itemize} + +Dissimilarity is defined as $$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:,$$ where $C$ stands for all clusters the data was clustered into. +For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. +Given this definition, the best way to cluster every dataset would be to put each object to its cluster. +That would not lead to any reasonable result. +Therefore there is a constraint added for clustering methods. +It can be either the maximum number of clusters or the maximum distance between two clusters. \\ -A straightforward example of clustering is a method called hierarchical clustering. +A straightforward example of clustering is a method called agglomerative hierarchical clustering. Given $N$ objects in a dataset, it creates $N$ clusters - meaning there is a cluster for each object. The method looks for two closest clusters and merges them into one. This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. @@ -357,12 +460,12 @@ \section{Introduction to Clustering}\label{sec:clust_intro} The algorithm stops when the centroids of clusters stop changing. Figure~\ref{alg:kmeans} shows the pseudocode of $K$-means. \begin{algorithm}\label{alg:kmeans} - \caption{k-Means pseudocode} + \caption{K-Means pseudocode} \label{k_mean_pseudocode} \begin{algorithmic}[1] - \INPUT set of examples $\alpha$ number of clusters $k$ - \STATE Randomly chose $k$ examples from $\alpha$ - \WHILE True + \INPUT set of examples $X$; number of clusters $k$ + \STATE Randomly chose $k$ examples from $X$ as centroids + \WHILE{True} \STATE Create $k$ clusters by assigning each object to the closest centroid \STATE Compute $k$ new centroids by averaging objects in each cluster \IF{Centroids do not change} @@ -370,11 +473,11 @@ \section{Introduction to Clustering}\label{sec:clust_intro} \ENDIF \ENDWHILE \end{algorithmic} -\end{algorithm} +\end{algorithm} \todo{better pseudocode, find better book} -This algorithm is fast, it has time complexity $\mathcal{O}(k*n*d)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. -It is the most commonly used clustering algorithm as it in most cases converges in a few iterations. -Some of the ideas from it are used in the delta medoids algorithm that is used for clustering in this thesis. \\ +This algorithm is fast, it has time complexity $\mathcal{O}(k \cdot n)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. +It is the most common clustering algorithm as it typically converges in a few iterations. +For more details about basic clustering algorithms see \cite{guttag2016introduction}.\\ %summary The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. @@ -382,25 +485,27 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Next section delves deeper into what it means when a measure does not form a metric space. \section{Clustering on Topological Space}\label{sec:cluster_topo} +This section explains the difference between metric and topological space and clustering approaches for topological spaces. \\ -A metric space is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X$ into $R$ which satisfies the following conditions: +A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X \to \mathbb{R}$\todo{ad pripominka, ze by to melo byt NxN, takhle to je popsane v te citovane knize. Prijde mi, ze je to obecnejsi a drzim se toho jak to popsal ten matematik.} which satisfies the following conditions: \begin{enumerate} \item [(i)] $d(x, y) \geq 0$; \item [(ii)] $d(x, y) = 0 \iff x = y$; \item [(iii)] $d(x, y) = d(y, x)$ - \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) for x, y, z \in X$. + \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \mathrm{for} x, y, z \in X$. \end{enumerate} -The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above and therefore, it does not form a metric space but a more general topological space. This is how \cite{stahl2014introduction} defines it. \\ +The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. +This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. \\ -Given any set $S$ a topology on S is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$, and with the following properties: +Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: \begin{enumerate} \item [(i)] The empty set $\emptyset$ is in $F$. \item [(ii)] The given set $S$ is in $F$. \item [(iii)] The intersection of any two sets of $F$ is in $F$. \item [(iv)] The union of any number of sets of $F$ is in $F$. \end{enumerate} -The pair ($S, F$) is called a topological space. \\ +The ordered pair ($S, F$) is called a topological space. \\ \todo{explain about neighborhoods} @@ -417,85 +522,87 @@ \section{Clustering on Topological Space}\label{sec:cluster_topo} Louvain method is a similar approach to clustering when we do not have a simple graph but a set of samples and a pairwise similarity measure. This method relies on creating a full similarity matrix for the whole dataset and then looking for communities in the data. \\ -In Cognitive Targeted Anomaly Detection Framework Louvain method cannot be used as calculating the full similarity matrix has a complexity of $\mathcal(n^2)$, which is impossible to calculate for a large network. +In Cognitive Targeted Anomaly Detection Framework Louvain method cannot be used directly as calculating the full similarity matrix has a complexity of $\mathcal(n^2)$, which is impossible to calculate for a large network. That is why an approximative clustering method is used. This method iteratively samples network hosts and runs the clustering algorithm on the sampled hosts. Each iteration creates or updates cluster prototypes. -If the data in the current batch fit into a previously prototyped cluster, they are added to it and the cluster prototype is updated. -If samples differ more than a predefined threshold a new cluster prototype is created. -%Cosine similarity represents the difference in the angle of two vectors. - -% jenom pairwise similarity!!! - nemuzeme delat kolecka - neplati geometricka reprezentace okoli -% centroid vubec neni v uvahu - casto nejde vubec udelat -% Louvian Modularity - sestaveni grafu na zaklade pairwise similarit - to co se pouziva v Threat - modularity clustering, community detection +If the data in the current batch fit into a previously prototyped cluster, they are added to it, and the cluster prototype is updated. +If samples differ more than a predefined threshold, a new cluster prototype is created. -%========================================CHAPTER================================== - -\chapter{Representative Selection} +\section{Representative Selection} -This chapter focuses on the idea of finding a representation of clusters. -Clustering huge datasets (millions on of objects) can result in big clusters of several tens of thousands of objects in them. +This section focuses on the idea of finding a representation of clusters. +Clustering huge datasets can result in big clusters of several tens of thousands of objects in them. Computational operations such as assigning new objects to clusters (e. g. $K$-Nearest Neighbors) are dependent on the number of objects in each cluster or the representation of these clusters. -If it were possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. \\ +If it was possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. \\ -Representation of clusters in Cognitive targeted anomaly detection framework is precisely a problem where computation on clusters needs to run in real-time, and therefore it is desirable to find a smaller representation of each given cluster. +A cluster prototype is a data sample that represents all samples in the data cluster. +According to \todo{ref Introduction to Data Mining}, there are three motivations for finding the most representative cluster prototypes: +\begin{itemize} + \item Summarization + \item Compression + \item Efficient Finding Nearest Neighbors +\end{itemize} +All of these apply to the NBAD aswell. +Therefore, summarizing the behavior of the whole cluster into a cluster prototype is desirable. +As is finding the smallest possible number of prototypes for each cluster for efficient finding nearest neighbors when adding new hosts to their corresponding clusters. \\ -\section{Definition of Representative Selection}\label{sec:def_rep_selection} -While having a large set of examples, representative selection aims to find minimal subset from these examples that carry sufficient information about the whole cluster. +\subsection{Definition of Representative Selection for Non-Metric Spaces}\label{sec:def_rep_selection} +Representative selection aims to find a minimal subset of examples from a cluster, that carries sufficient information about the whole cluster. This problem was well defined in \cite{liebman2015representative} and the following definition is taken from that paper. - Let $S$ be a data set, $d : S \times S \to \mathbb{R} +$ be a distance measure (not necessarily a metric), and $\delta$ be a distance threshold below which samples are considered sufficiently similar. -The task is finding a representative subset $C \subset S$that best encapsulates the data. +The task is finding a representative subset $Z \subset S$ that best encapsulates the data. Two following requirements are imposed on an algorithm for finding a representative subset: \begin{itemize} - \item \textbf{Requirement 1}: The algorithm must return a subset $C \subset S$ such that for any sample $x \in S$, there exists a sample $c \in C$ satisfying $d(x, c) \le \delta$. + \item \textbf{Requirement 1}: The algorithm must return a subset $Z \subset S$ such that for any sample $x \in S$, there exists a sample $z \in C$ satisfying $d(x, z) \le \delta$. \item \textbf{Requirement 2} : The algorithm cannot rely on a metric representation of the samples in $S$. \end{itemize} To compare the quality of different subsets returned by different algorithms, two criteria are measured: \begin{itemize} - \item \textbf{Criterion 1}: $|C|$ - seeking the smallest possible subset C that satisfies Requirement 1:. + \item \textbf{Criterion 1}: $|Z|$ - seeking the smallest possible subset $Z$ that satisfies Requirement 1:. \item \textbf{Criterion 2}: Representative should best fit the data on average. Given representative subsets of equal size, the preference is on the one that minimizes the average distance of samples from their respective representatives. \end{itemize} -Criteria 1 and 2 are applied on a representative set solution. +Criteria 1 and 2 are applied to a representative set solution. In addition, the following desiderata for a representative selection algorithm are expected. \begin{itemize} - \item \textbf{Desideratum 1}: Stable representative selection algorithms are preferred. Let $C_1$ and $C_2$ be different representative subsets for dataset $S$ obtained by two different runs of the same algorithm. -Stability is defined as the overlap $\frac{|C_1 \cap C_2|}{|C_1 \cup C_2|}$ - The higher the expected overlap is, the more stable the -algorithm is. This desideratum ensures the representative set is robust to -randomization in data ordering or the choices made by the algorithm. + \item \textbf{Desideratum 1}: Stable representative selection algorithms are preferred. Let $Z_1$ and $Z_2$ be different representative subsets for dataset $S$ obtained by two different runs of the same algorithm. +Stability is defined as the overlap $\frac{|Z_1 \cap Z_2|}{|Z_1 \cup Z_2|}$ The higher the expected overlap is, the more stable the algorithm is. +This desideratum ensures the representative set is robust to randomization in data ordering or the choices made by the algorithm. \item \textbf{Desideratum 2}: The algorithm should be efficient and scale well for large datasets. \end{itemize} -This definition of representative selection proble serves well for this paper. +This definition of representative selection problem serves well for this paper. -\section{Representative Selection in a Metric Space}\label{sec:rep_select_metric} -Representative selection in metric space relies heavily on being able to create centroids. -A centroid is a point in space that is not necessarily part of the cluster but sits in the center of the cluster. -A centroid is found by averaging all points of given cluster thus finding a -% popsani jak by se to delalo normalne -% stezejni myslenky proc to delame takto +\subsection{Representative Selection in a Metric Space}\label{sec:rep_select_metric} +\todo{Centroid by si mel zavest a vysvetlit vyhody a nevyhody. Centroid muze byt vazeny nebo aritmeticky prumer, to dokazes zapsat vzorcem a bude to hezky vypadat nebo obrazek nebo tak neco. Medoid si vubec nevysvetlil co je.} +A dataset that has the same number of features for each sample and a metric that fulfills all the requisites shown in \todo{reference the definition} is an example of a dataset in a metric space. +Selecting a prototype from this dataset is often best achieved by calculating a centroid for a given cluster. +A centroid can be, for example, the average of all points in the cluster. +If such a centroid is meaningful (it does not fall away from the data), it can be selected as a prototype. +However, if calculated centroid is not meaningful (it falls outside of the dataset), a medoid can be selected as a prototype. +A medoid is the most representative point of a cluster. -\section{Representative Selection in an Arbitrary Space}\label{sec:rep_select_arbitrary} +\subsection{Representative Selection in a Topological Space}\label{sec:rep_select_arbitrary} +Clearly, the concept of centroid explained in the previous section cannot be used to solve the problem of finding a representative in an arbitrary space (i.e., topological space). +Medoids, of course, can be selected for datasets in arbitrary spaces. \\ -%What are the algorithms that can be used for representative selection in non-metric spaces? -%Problems that need to be tackled using this method. - -%========================================CHAPTER================================== +$K$-Medoids algorithm that uses the concept of medoids instead of centroids that are used in the $K$-Means algorithm. -\chapter{Implementation and Performance Testing of Relevant Methods} -This chapter explains the implementation and testing of selected methods in multiple different scenarios. -In the first section, algorithms for selected methods are listed and explained in detail. -The following section serves as a list of used datasets ranging from general to more specific ones. -The last section of this chapter talks about the tests that were run and explains why they were chosen. +\todo{general approaches} -The programming language chosen for implementation was Python 3.7. -It allowed different libraries for data manipulation like Pandas library and also is an excellent choice for testing a prototype as the implementation of different algorithms is not complicated. +There are not many well-known approaches to solve this problem. +The one that is used in this thesis is explained in \cite{liebman2015representative}. +They took the main ideas of $K$-Means clustering and transformed them for usage in a non-metric space with a pair-wise similarity measure. +Instead of stating the $K$ in advance they state a parameter $ 0 \leq \delta \leq 1$ that serves as a constraint. +Then they separate the cluster into subclusters based on this parameter. +Each of these subclusters is represented by a medoid. +A set of these medoids then serves as a cluster prototype for the whole cluster. +This method is explained in greater detail in the Section~\ref{subsec:delta_medoids}. -\section{Methods Relevant to Cognitive Anomaly Detection}\label{sec:relevant_methods} +\section{Algorithms Relevant for Topological Space}\label{sec:relevant_methods} Based on the previously explained approaches for finding representatives of clusters in non-metric, spaces the following methods were chosen, tested and compared as a part of this thesis. \begin{itemize} \item Random selection @@ -506,46 +613,42 @@ \section{Methods Relevant to Cognitive Anomaly Detection}\label{sec:relevant_met \subsection{Random Selection}\label{subsec:random_select} -This is the algorithm that serves as a baseline to measure the improvement as it is the method currently used in Cognitive targeted anomaly detection framework. -It simply selects a given number of representatives from the cluster randomly. \\ +Random selection algorithm serves as a baseline to measure the improvement as it is the method currently used in Cognitive targeted anomaly detection framework. +It selects a given number of representatives from the cluster randomly. \\ -In clusters that overlap a lot, selecting random samples from each cluster leads to undesired noise. -However as experimental results presented in \todo{citation Kopp paper} in the domain of computer networks the overlap of clusters is not great. -It, of course, depends on each individual network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. \\ +In clusters dense clusters, selecting random samples from each cluster leads to undesired noise. +However as experimental results presented in \cite{kopp2018community} in the domain of computer networks the density of clusters is not significant. +It, of course, depends on each network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. \\ For this thesis, the random selection algorithm serves as a baseline in comparing the results to other methods. -The number of samples selected is decided by the number of the full Delta-Medoids algorithm. \subsection{Greedy Selection}\label{subsec:greedy_select} - -The greedy selection algorithm is a simple straightforward algorithm that selects a random sample from the set as the first representative. -Then it looks for the most dissimilar sample in the rest of the dataset. +\todo{obrazky sem a do dalsi sekce} +The greedy selection algorithm is a straightforward algorithm that selects a random sample from the set as the first representative. +Then it looks for the most different sample in the rest of the dataset. Once the desired sample is found, the algorithm removes all neighboring samples with at least the similarity of $\delta$ from the set. -Then it looks for other dissimilar samples until the whole set is covered by thus selected representatives. \\ \todo{pseudocode} +Then it looks for other dissimilar samples until the whole set is represented. \\ \todo{pseudocode} The speed of this algorithm depends on the sparsity of the given set. -In the worst case scenario, all points would be selected as representatives while always calculating the distance to each remaining sample in the set. -Thus reaching the time complexity of $\mathcal{O}(n^2)$. +In the worst case scenario, all points would be selected as representatives while always calculating the distance to each remaining sample in the set. Then it would reach the time complexity of $\mathcal{O}(n^2)$. \subsection{$\delta$-Medoids}\label{subsec:delta_medoids} The two following algorithms were first eplained in \cite{liebman2015representative}. -They were tested on two different problems that also use clustering in non-metric space - computing distance of two musical segments and comparing trajectories of objects. -As was explained in Chapter 2, the term centroid is not relevant in arbitrary spaces, therefore the term medoid is used. -Medoid is a sample that is part of the cluster and that resembles most of the points in its adjacency. \\ +They were tested on two different problems that also use clustering in non-metric space - computing distance of two musical segments and comparing trajectories of objects.\\ -$\delta$-Medoids algorithms try to find the least of points that are needed to cover the cluster with only one given constraint of the distance of representatives - $\delta$. +$\delta$-Medoids algorithm tries to find a minimal subset of points that are needed to cover the cluster with only one given constraint, that distance of the representatives would be at least $\delta$. There are two versions of this algorithm. One-shot version is faster but less precise than the full $\delta$-medoids algorithm. -\subsubsection{$\delta$-Medoids One-Shot} +\subsection{$\delta$-Medoids One-Shot} The idea behind this algorithm is to go through the dataset looking for representatives that differ at least by the given parameter $\delta$ from each previously selected representatives. -By taking this approach it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. \\ +By taking this approach, it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. \\ The algorithm is shown in Figure~\ref{alg:delta_medoids_one_shot}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. -\begin{algorithm} +\begin{algorithm}\label{alg:delta_medoids_one_shot} \caption{$\delta$-Medoids One-shot} \label{delta_medoids_one_shot} \begin{algorithmic}[1] @@ -566,21 +669,21 @@ \subsubsection{$\delta$-Medoids One-Shot} \end{algorithm} -Selection of the medoids to represent the cluster is strongly influenced by the choice of the first representative. +The choice of the first representative strongly influences the selection of the medoids to represent the cluster. In some cases, this one-shot approach could be misleading. -\subsubsection{$\delta$-Medoids} +\subsection{$\delta$-Medoids} The full version of the algorithm runs a one-shot algorithm multiple times. Each time it goes through the dataset it selects a better medoid than before. Once two passes through the data do not change any medoid, the algorithm stops. This algorithm is shown in Figure~\ref{alg:delta_medoids_full}.\\ -This approach is clearly slower, as the main routine from one shot algorithm has to be run multiple times. +This approach is slower, as the main routine from one shot algorithm has to be run multiple times. \todo{Mention convergence of the algorithm.} On the other hand, it is able to select better medoids, thus getting rid of the difficult choice of the first representative to select. -\begin{algorithm} +\begin{algorithm}\label{alg:delta_medoids_full} \caption{$\delta$-Medoids} \label{delta_medoids_full} \begin{algorithmic}[1] @@ -617,6 +720,16 @@ \subsubsection{$\delta$-Medoids} \end{algorithmic} \end{algorithm} +%========================================CHAPTER================================== + +\chapter{Datasets}\label{ch:performance} + +This chapter explains the implementation and testing of selected methods in multiple different scenarios. +In the first section, algorithms for selected methods are listed and explained in detail. +The following section serves as a list of used datasets ranging from general to more specific ones. +The last section of this chapter talks about the tests that were run. +It also explains why the choice of these tests. + \section{Datasets}\label{sec:datasets} The task of cluster representation in non-metric spaces generally is not necessarily connected only to the Security field. That is why the selected algorithms were tested on artificially created datasets, clustering datasets previously used in literature and on specific data collected from a real network. @@ -627,56 +740,89 @@ \section{Datasets}\label{sec:datasets} \subsection{Datasets Created for this Work} -These datasets were created solely for this thesis and they served as a sanity check for each of the method. +These datasets were created to test specific properties of tested methods. The datasets created are the following: \begin{itemize} \item Blobs \item Blobs 3D \item Overlap \item Noisy Circles - \item Noisy Circles 3D + \item Noisy Circles 3D \todo{add spiral dataset} \end{itemize} -All of these datasets are in 3-dimensional space so that they could be easily visualized. \todo{add pictures} +All of these datasets are in space of two or three dimensions. +They can be easily visualized on graphs and nicely show some of the specific properties of implemented algorithms.\todo{add pictures} Similarity measure used in these datasets was Euclidean distance. \\ -The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter X. \todo{Link to chapter} The Overlap dataset consists of 3 clusters in shapes of blocks. Noisy Circles and Noisy Circles 3D are datasets are one of the hardest datasets to represent even by methods that are designed specifically for metric space. +The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter X. \todo{Link to chapter} The Overlap dataset consists of 3 clusters in shapes of blocks. +These blocks partly overlap to test how selected algorithms cope with overlapping clusters. Noisy Circles and Noisy Circles 3D are datasets are one of the hardest datasets to represent even by methods that are designed specifically for metric space. +These two datasets were chosen to test the resistance to noise in selecting representatives. There are two clusters as shown in figure X. \todo{add figure} \subsection{Image Recognition Datasets} One of the best-documented areas of clustering is the area of image recognition. -That is why 2 datasets from this area were chosen for measuring the precision and complexity of each method. Both of them are labeled datasets of features collected from digits written by human hand. \\ +That is why two datasets from this area were chosen for measuring the precision and complexity of each method. Both of them are labeled datasets of features collected from digits written by human hand. \\ + +The Pen-Based Recognition of Handwritten Digits Data Set \cite{dua1998pendigit} consists of \todo{explain what it is} +The Multiple Features Data Set \cite{dua1998pendigit} consist of \todo{explain how is it different} \todo{add MNIST} \subsection{Real Network Dataset} -Confidential dataset of an anonymized network in one real company was used for finetuning and testing the methods on a real scenario. +Real network traffic captured from a company with approximately 20k employees. +The capture is from 24 hours of a working day a consists of \todo{count flows} flows. This dataset was created from proxy logs in that company following the workflow explained in section x \todo{Link section.}. \\ -\section{Testbed} -Testing of each method was done in several steps: -Data preparation - creation of pandas dataframes and loading it into special Cluster Class. -Cross-validation of each method. 80% of samples used for training, 20% for testing. -Testing was done by $K$-Nearest Neighbor with $K = 1$, and creation of confusion matrix while calculating precision and recall. - -The testbed application was implemented in Python 3 using the Jupyter notebook technology. -It consists of 3 steps: -\begin{itemize} - \item Loading data into TestDF - \item Training model on 80% of dataset sample - \item Testing by k-Nearest Neighbor classification method on the remaining 20% -\end{itemize} - -\subsection{Loading Data} -Firstly, it is needed to load data from a CSV file and prepare them for further computation. -Pandas library for Python 3 is used for it in implementation. - %========================================CHAPTER================================== -\chapter{Results} -% Testbed explanation - why is it the best etc. +\chapter{Experiments} + +In this chapter each experiment that was run. +Each section is dedicated to one experiment. +For each experiment, the motivation, experimental setup, results and conclusion are listed. -What were the parameters when this method has been used? -How did it perform compared to random selection? +\todo{Struktura popisu jednotlivych experimantu jako sekce. +1) motivace toho experiment - proc delame ten experiment, co chceme zjistit +2) experimental setup - jaky datset + jake algorimty a jejich nastaveni +3) komentovane vysledky +4) shrnuti co jsme se dozvedeli} +\section{Testbed} +The testbed application was implemented in Python 3 using the Jupyter notebook technology. +It consists of three steps: +\begin{itemize} + \item Loading training and test data. + \item Selecting representatives from the train data. + \item Testing by assigning test data to clusters based on representatives selected. +\end{itemize} +The real implementation differs a bit in the structure of data for real network dataset and for the datasets from different clustering fields. +However, the main ideas remain the same. \\ + +\subsection{Loading Data}\label{subsec:load_data} +The data is loaded from CSV files and prepared for further computation. +Each sample from the dataset must be a part of some cluster with a unique identification. +The data loaded is separated into two parts. +The first part, approximately 80\% of all data is labeled as data for training. +The training data is used for selecting representatives by methods that were listed in Section~\ref{sec:relevant_methods}. +The remaining data is labeled as data for testing. +It is used later on for testing what method selected the best representatives for each cluster. +\todo{where to put cross-validation} + +\subsection{Training the Model} +After the data is loaded each cluster is used as an input for each of the four methods that were found relevant. +The important thing is that the full $\delta$-Medoids algorithm is run before the random selection algorithm. +The numbers of representatives selected via random selection algorithm are decided by the size of results of full $\delta$-Medoids. \todo{Random selection repetition??} +This process is repeated for each cluster and the results are combined into one DataFrame for each method. +This way, each method has selected a subset of all training data as representatives. +After the process is complete, all of the training data was covered by representatives that are part of corresponding clusters. +As explained in Section \todo{Medoid!} all of these representatives are medoids - they are part of the clusters. + +\subsection{Testing the Quality of Selected Representatives} +When each cluster has its own representatives found with each method, the testing can start. +Testing data is a part of overall dataset put aside in ~\ref{subsec:load_data}. +For each sample used for testing, it is known to what cluster it belongs to. +The algorithm used for testing the is $K$-Nearest Neighbors, where the test data samples are assigned to representatives selected in the training phase. +The $K$ selected for testing is 1. \todo{test other K, and then explain why 1} +Result of $K$-Nearest Neighbors algorithm is then compared to the correct value. +A confusion matrix is created for better visualization and precision and recall is calculated. \setsecnumdepth{part} From 73f69c801e0ee0d97070d60dea62ebef0c08d3c9 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Tue, 7 May 2019 17:10:12 +0200 Subject: [PATCH 28/65] added first experiments to text --- text/hlavac-thesis.tex | 111 ++++++++++++++--------- text/img/delta_medoids_select.png | Bin 0 -> 46596 bytes text/img/delta_medoids_select_better.png | Bin 0 -> 44294 bytes 3 files changed, 68 insertions(+), 43 deletions(-) create mode 100644 text/img/delta_medoids_select.png create mode 100644 text/img/delta_medoids_select_better.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 0728992..d77c991 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -747,7 +747,8 @@ \subsection{Datasets Created for this Work} \item Blobs 3D \item Overlap \item Noisy Circles - \item Noisy Circles 3D \todo{add spiral dataset} + \item Noisy Circles 3D + \item Moons \end{itemize} All of these datasets are in space of two or three dimensions. They can be easily visualized on graphs and nicely show some of the specific properties of implemented algorithms.\todo{add pictures} @@ -774,55 +775,79 @@ \subsection{Real Network Dataset} %========================================CHAPTER================================== \chapter{Experiments} - In this chapter each experiment that was run. Each section is dedicated to one experiment. For each experiment, the motivation, experimental setup, results and conclusion are listed. -\todo{Struktura popisu jednotlivych experimantu jako sekce. -1) motivace toho experiment - proc delame ten experiment, co chceme zjistit -2) experimental setup - jaky datset + jake algorimty a jejich nastaveni -3) komentovane vysledky -4) shrnuti co jsme se dozvedeli} +All experiments were made in Jupyter Notebook technology using Python 3.7 kernel. +For loading data from files and storing them in memory was used the Pandas library version 0.23.4\todo{link to documentation}. +These technologies were chosen due to this thesis's experimental nature, as the goal was to produce a prototype solution for this problem. + +\section{Experiment 1}\label{sec:exp1}\todo{name differently than Experiment?} -\section{Testbed} -The testbed application was implemented in Python 3 using the Jupyter notebook technology. -It consists of three steps: +The first experiment was run on easily the data specifically created for this thesis. +It served as a sanity check of the proposed methods. +The methods that were explained in the previous chapter\todo{make reference} are designed to work in non-metric space but they still work in a metric space. +Using them on metric data and visualizing the results helped a lot to check the assumptions about results experimentally. + +\subsection{Experimental Setup} +Datasets used for this experiment were: \begin{itemize} - \item Loading training and test data. - \item Selecting representatives from the train data. - \item Testing by assigning test data to clusters based on representatives selected. + \item Blobs 3D + \item Overlap + \item Moons + \item Noisy Circles 3D \end{itemize} -The real implementation differs a bit in the structure of data for real network dataset and for the datasets from different clustering fields. -However, the main ideas remain the same. \\ - -\subsection{Loading Data}\label{subsec:load_data} -The data is loaded from CSV files and prepared for further computation. -Each sample from the dataset must be a part of some cluster with a unique identification. -The data loaded is separated into two parts. -The first part, approximately 80\% of all data is labeled as data for training. -The training data is used for selecting representatives by methods that were listed in Section~\ref{sec:relevant_methods}. -The remaining data is labeled as data for testing. -It is used later on for testing what method selected the best representatives for each cluster. -\todo{where to put cross-validation} - -\subsection{Training the Model} -After the data is loaded each cluster is used as an input for each of the four methods that were found relevant. -The important thing is that the full $\delta$-Medoids algorithm is run before the random selection algorithm. -The numbers of representatives selected via random selection algorithm are decided by the size of results of full $\delta$-Medoids. \todo{Random selection repetition??} -This process is repeated for each cluster and the results are combined into one DataFrame for each method. -This way, each method has selected a subset of all training data as representatives. -After the process is complete, all of the training data was covered by representatives that are part of corresponding clusters. -As explained in Section \todo{Medoid!} all of these representatives are medoids - they are part of the clusters. - -\subsection{Testing the Quality of Selected Representatives} -When each cluster has its own representatives found with each method, the testing can start. -Testing data is a part of overall dataset put aside in ~\ref{subsec:load_data}. -For each sample used for testing, it is known to what cluster it belongs to. -The algorithm used for testing the is $K$-Nearest Neighbors, where the test data samples are assigned to representatives selected in the training phase. -The $K$ selected for testing is 1. \todo{test other K, and then explain why 1} -Result of $K$-Nearest Neighbors algorithm is then compared to the correct value. -A confusion matrix is created for better visualization and precision and recall is calculated. + +All 4 algorithms were run. +The $\delta$ was estimated as \todo{how is median at lower ten percent called, put it to maths} times 1.05 from distances from one point in the data to all of the others. + +\subsection{Results} + +The results for different methods are listed below. + +\subsection{Conclusion} +At first glance, all the confusion matrices look almost perfect and as expected. +However, when the results were visualized for one cluster, the delta medoids algorithm did not result in expected selection. +As can be seen on Figure~\ref{img:blobs_border_select}, the algorithm picked the samples on the edge of the dataset that did not cover many points in their neighborhoods. +\begin{figure} + \includegraphics[width=\linewidth]{img/delta_medoids_select.png} + \label{img:blobs_border_select} + \caption{$\delta$-Medoids choosing the border samples of a cluster} +\end{figure} +Selecting the border samples clearly was not intended and the $\delta$-Medoids algorithm had to select only the relevant samples. +The relevance of a representative was determined to the number of points that fit into its $\delta$-neighborhood. \todo{where to list the changes, here or in the theoretical chapter, theory better I think} + +\section{Experiment 2}\label{sec:exp2} + +This experiment was run to test the changes made in the $\delta$-Medoids algorithm. +It was updated not to select the border samples, as is explained in section\todo{ref the section where I explain it}. + +\subsection{Experimental Setup} +Datasets used for this experiment were: +\begin{itemize} + \item Blobs 3D + \item Overlap + \item Moons + \item Noisy Circles 3D +\end{itemize} + +All 4 algorithms were run. +The $\delta$ was estimated as \todo{how is median at lower ten percent called, put it to maths} times 1.05 from distances from one point in the data to all of the others. + +\subsection{Results} + +The results for different methods are listed below. + +\subsection{Conclusion} + +The results of this experiment empirically supported the expected result of not selecting the border samples from a dataset as can be seen on Figure~\ref{img:blobs_good_select} +\begin{figure} + \includegraphics[width=\linewidth]{img/delta_medoids_select_better.png} + \label{img:blobs_good_select} + \caption{$\delta$-Medoids choosing only relevant samples of a cluster} +\end{figure} +This sanity check by visualization fulfilled all of the expectation. \setsecnumdepth{part} diff --git a/text/img/delta_medoids_select.png b/text/img/delta_medoids_select.png new file mode 100644 index 0000000000000000000000000000000000000000..47c5cdfc29f97d9435768c1df718c5157424ea22 GIT binary patch literal 46596 zcmbrm2RPOL-v|8FQdBx2*~Kpj8D(WBBW2HIlPwaVgL6)aWLC%?*^bPz9b}fh_Xybz 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z9@Z0(5N3te$H`{DI*(yq`XOX-FZy5nv35yQ&l|QP9c5C{wri{G#Vh6CK29R%?5b-gsSY>+~6v>D;|uay>Xt zCt{7*R^M3D?3>FHbkdC`6k=|h*$cHgFQTR?^3ilbYe_!l$))7vxTVOgxs>dqPIL&b zUnWeRKqIL`sXTtGR?&q;LzzT^LtQS`^F7fB4U@H5duLXA^Hf$SrzE1h&FtRCg9qI!pd=~ie_OPG-zyotgIRzyqjH4YM zpvf!@i^Z@l;kv_ZkyX~T@bd0nus#?Sm=!tuo`Nmy0th>wEma?RhAf>OR6f;NsZJk)q!)DBBFi1osbh4 z13OTMx`@Ya$a9_O7N)^)q(Gg$yGv0#uTv}5&7P{wTrCvh}WO|RcQ j-3tB_!Kvy0*}S!ojZ Date: Wed, 8 May 2019 10:27:02 +0200 Subject: [PATCH 29/65] added results to Experiment 2 --- text/hlavac-thesis.tex | 159 +++++++++++++++++++++++++++++++++++++- text/img/exp1_blobs.png | Bin 0 -> 23605 bytes text/img/exp1_circles.png | Bin 0 -> 24215 bytes text/img/exp1_moons.png | Bin 0 -> 22300 bytes text/img/exp1_overlap.png | Bin 0 -> 36520 bytes text/img/exp2_blobs.png | Bin 0 -> 23605 bytes text/img/exp2_circles.png | Bin 0 -> 22544 bytes text/img/exp2_moons.png | Bin 0 -> 21980 bytes text/img/exp2_overlap.png | Bin 0 -> 36125 bytes 9 files changed, 157 insertions(+), 2 deletions(-) create mode 100644 text/img/exp1_blobs.png create mode 100644 text/img/exp1_circles.png create mode 100644 text/img/exp1_moons.png create mode 100644 text/img/exp1_overlap.png create mode 100644 text/img/exp2_blobs.png create mode 100644 text/img/exp2_circles.png create mode 100644 text/img/exp2_moons.png create mode 100644 text/img/exp2_overlap.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index d77c991..7feb950 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -34,6 +34,9 @@ \algnewcommand\OUTPUT{\item[\textbf{Output:}]} \algdef{SE}[DOWHILE]{DO}{DOWHILE}{\algorithmicdo}[1]{\algorithmicwhile\ #1}% +\newcommand{\specialcell}[2][l]{% +\begin{tabular}[#1]{@{}l@{}}#2\end{tabular}} + %\usepackage[main=english,czech]{babel} % % list of acronyms @@ -789,6 +792,8 @@ \section{Experiment 1}\label{sec:exp1}\todo{name differently than Experiment?} It served as a sanity check of the proposed methods. The methods that were explained in the previous chapter\todo{make reference} are designed to work in non-metric space but they still work in a metric space. Using them on metric data and visualizing the results helped a lot to check the assumptions about results experimentally. +Also, the need for selection of few representatives from each dataset was tested. +It is expected, for these simple clusters, not to exceed 30 selected representatives. \subsection{Experimental Setup} Datasets used for this experiment were: @@ -804,14 +809,90 @@ \subsection{Experimental Setup} \subsection{Results} -The results for different methods are listed below. +The results for each dataset are listed below. +\textbf{Blobs 3D} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 400 & 21 & 23 & 23 & 23 \\ +B & 400 & 17 & 20 & 20 & 20 \\ +C & 400 & 19 & 20 & 20 &20 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Blobs 3D dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp1_blobs.png} + \label{img:exp1_blobs} + \caption{Confusion matrices for Blobs 3D dataset} +\end{figure} + +\textbf{Overlap} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 800 & 25 & 23 & 23 & 23 \\ +B & 960 & 26 & 21 & 21 & 21 \\ +C & 640 & 22 & 24 & 24 & 24 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Overlap dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp1_overlap.png} + \label{img:exp1_overlap} + \caption{Confusion matrices for Overlap dataset} +\end{figure} + +\textbf{Moons} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 600 & 18 & 19 & 19 & 19 \\ +B & 600 & 19 & 18 & 18 & 18 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Moons dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp1_moons.png} + \label{img:exp1_moons} + \caption{Confusion matrices for Moons dataset} +\end{figure} + +\textbf{Circles} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 600 & 14 & 14 & 14 & 14 \\ +B & 600 & 8 & 7 & 7 & 7 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Circles dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp1_circles.png} + \label{img:exp1_circles} + \caption{Confusion matrices for Circles dataset} +\end{figure} + \subsection{Conclusion} At first glance, all the confusion matrices look almost perfect and as expected. +The number of representatives selected did not exceed 30 in any cluster. +The number of missed classification is low even in overlapping or complex datasets. However, when the results were visualized for one cluster, the delta medoids algorithm did not result in expected selection. As can be seen on Figure~\ref{img:blobs_border_select}, the algorithm picked the samples on the edge of the dataset that did not cover many points in their neighborhoods. \begin{figure} - \includegraphics[width=\linewidth]{img/delta_medoids_select.png} + \includegraphics[width=\linewidth]{img/delta_medoids_select.png} \label{img:blobs_border_select} \caption{$\delta$-Medoids choosing the border samples of a cluster} \end{figure} @@ -838,6 +919,80 @@ \subsection{Experimental Setup} \subsection{Results} The results for different methods are listed below. +\textbf{Blobs 3D} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 400 & 23 & 24 & 24 & 24 \\ +B & 400 & 21 & 22 & 22 & 22 \\ +C & 400 & 20 & 21 & 21 & 21 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Blobs 3D dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp2_blobs.png} + \label{img:exp2_blobs} + \caption{Confusion matrices for Blobs 3D dataset} +\end{figure} + +\textbf{Overlap} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 800 & 22 & 21 & 21 & 21 \\ +B & 960 & 21 & 20 & 20 & 20 \\ +C & 640 & 19 & 19 & 19 & 19 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Overlap dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp2_overlap.png} + \label{img:exp2_overlap} + \caption{Confusion matrices for Overlap dataset} +\end{figure} + +\textbf{Moons} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 600 & 21 & 18 & 18 & 18 \\ +B & 600 & 16 & 20 & 20 & 20 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Moons dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp2_moons.png} + \label{img:exp2_moons} + \caption{Confusion matrices for Moons dataset} +\end{figure} + +\textbf{Circles} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 600 & 15 & 15 & 15 & 15 \\ +B & 600 & 8 & 8 & 8 & 8 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Circles dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp2_circles.png} + \label{img:exp2_circles} + \caption{Confusion matrices for Circles dataset} +\end{figure} + \subsection{Conclusion} diff --git a/text/img/exp1_blobs.png b/text/img/exp1_blobs.png new file mode 100644 index 0000000000000000000000000000000000000000..7a8c591042d1614220123085ee316bf323e6ac69 GIT binary patch literal 23605 zcmeI4d010-x9>r%)T;2dR(%nHwggcTsEjh(TE&QfRhcK1NyaFn%vxKh10Z3N8LNmg z50c0jECPlQKrjgq2+9m$On?}Mh{#Ut=^xIEh-^8A{(QO>zBBzduQ>R!#qWFU#ZTeCuuq+Tg@13m zcG}ucL}dFH=#OaEK*Sn+Q``TfmH!2ttAEgCUl$SY%l_BAaQZ-s0H60a|fBOYxoUfZoRQ9{=A|hXjV1M}T;*E^yf$)d4fK1^6_Ya(~6%LxO^~sM^7&_e%?>XT~hWwQCDlWx8~%) 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Date: Wed, 8 May 2019 16:50:45 +0200 Subject: [PATCH 30/65] Added part of the third experiment. --- text/hlavac-thesis.tex | 76 ++++++++++++++++++++++++++++++------ text/img/exp3_pendigits.png | Bin 0 -> 88813 bytes 2 files changed, 65 insertions(+), 11 deletions(-) create mode 100644 text/img/exp3_pendigits.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 7feb950..71ce924 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -721,11 +721,11 @@ \subsection{$\delta$-Medoids} \ENDFOR \DOWHILE{$representatives_t = representatives_{t-1}$} \end{algorithmic} -\end{algorithm} +\end{algorithm} \todo{add the hack for better results} %========================================CHAPTER================================== -\chapter{Datasets}\label{ch:performance} +\chapter{Datasets}\label{ch:datasets} This chapter explains the implementation and testing of selected methods in multiple different scenarios. In the first section, algorithms for selected methods are listed and explained in detail. @@ -743,14 +743,12 @@ \section{Datasets}\label{sec:datasets} \subsection{Datasets Created for this Work} -These datasets were created to test specific properties of tested methods. +These datasets were created to test the specific properties of tested methods. The datasets created are the following: \begin{itemize} - \item Blobs \item Blobs 3D \item Overlap - \item Noisy Circles - \item Noisy Circles 3D + \item Circles 3D \item Moons \end{itemize} All of these datasets are in space of two or three dimensions. @@ -764,9 +762,14 @@ \subsection{Datasets Created for this Work} \subsection{Image Recognition Datasets} One of the best-documented areas of clustering is the area of image recognition. -That is why two datasets from this area were chosen for measuring the precision and complexity of each method. Both of them are labeled datasets of features collected from digits written by human hand. \\ +That is why two datasets from this area were chosen for measuring the precision and complexity of each method. +Both of them are labeled datasets of features collected from digits written by human hand. \\ -The Pen-Based Recognition of Handwritten Digits Data Set \cite{dua1998pendigit} consists of \todo{explain what it is} +The Pen-Based Recognition of Handwritten Digits Data Set \cite{dua1998pendigit} consists of 10922 samples with 16 attributes. +Each sample represents features collected from a handwritten digit. +Each digit is represented by a little bit more than a thousand samples. +Features represent coordinate information about the digits as they were written on a $500\times500$ pixel frame. +This dataset is split to train dataset (70\%) and test dataset (30\%). The Multiple Features Data Set \cite{dua1998pendigit} consist of \todo{explain how is it different} \todo{add MNIST} @@ -1004,6 +1007,57 @@ \subsection{Conclusion} \end{figure} This sanity check by visualization fulfilled all of the expectation. +\section{Experiment 3}\label{sec:exp3} + +After the algorithms were tested on visualizable data, the experiments with well known labeled datasets could start. +The motivation for this experiment was to test the algorithm results on well explored and well-annotated datasets commonly used as a benchmark for clustering. + +\subsection{Experimental Setup} +Datasets used for this experiment were: +\begin{itemize} + \item Pendigit + \item +\end{itemize} + +All 4 algorithms were run. +The $\delta$ was estimated as \todo{how is median at lower ten percent called, put it to maths} times 1.05 from distances from one point in the data to all of the others. + +\subsection{Results} + +The results for different methods are listed below. + +\textbf{Pendigits} + +\begin{table}[] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +0 & 780 & 33 & 33 & 33 & 33 \\ +1 & 779 & 33 & 41 & 41 & 41 \\ +2 & 780 & 17 & 15 & 15 & 15 \\ +3 & 719 & 12 & 9 & 9 & 9 \\ +4 & 780 & 25 & 24 & 24 & 24 \\ +5 & 720 & 25 & 27 & 27 & 27 \\ +6 & 720 & 16 & 16 & 16 & 16 \\ +7 & 778 & 22 & 20 & 20 & 20 \\ +8 & 719 & 75 & 77 & 15 & 15 \\ +9 & 719 & 50 & 52 & 13 & 13 \\ \hline +\end{tabular} +\caption{Number of selected representatives for Pendigit dataset} +\end{table} + +\begin{figure} + \includegraphics[width=\linewidth]{img/exp3_pendigits.png} + \label{img:exp3_pendigits} + \caption{Confusion matrices for Pendigits dataset} +\end{figure} + +\subsection{Conclusion} +Using algorithms on real datasets started to show more significant differences between results in algorithms. +An interesting thing is that Greedy Selection and $\delta$-Medoids One-Shot select similar number of representatives and their results are also very similar. +This is interesting as the complexity of these algorithms differs a lot, Greedy Selection being much slower with $\mathcal(n^2)$. \todo{better wording of this conclusion} + + \setsecnumdepth{part} %========================================CHAPTER================================== @@ -1029,11 +1083,11 @@ \chapter{Conclusion} \setsecnumdepth{all} \appendix -\chapter{Acronyms} +\chapter{Abreviations} % \printglossaries \begin{description} - \item[GUI] Graphical user interface - \item[XML] Extensible markup language + \item[NIDS] Intrusion Detection System + \item[NBAD] Network-Based Anomaly Detection \end{description} diff --git a/text/img/exp3_pendigits.png b/text/img/exp3_pendigits.png new file mode 100644 index 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-> 418133 bytes 2 files changed, 6 insertions(+), 1 deletion(-) create mode 100644 text/img/datasets.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 71ce924..97dc498 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -752,7 +752,12 @@ \subsection{Datasets Created for this Work} \item Moons \end{itemize} All of these datasets are in space of two or three dimensions. -They can be easily visualized on graphs and nicely show some of the specific properties of implemented algorithms.\todo{add pictures} +They can be easily visualized on graphs and nicely show some of the specific properties of implemented algorithms. +\begin{figure} + \includegraphics[width=\linewidth]{img/datasets.png} + \label{img:datasets} + \caption{Graphs of all datasets} +\end{figure} Similarity measure used in these datasets was Euclidean distance. \\ The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter X. \todo{Link to chapter} The Overlap dataset consists of 3 clusters in shapes of blocks. diff --git a/text/img/datasets.png b/text/img/datasets.png new file mode 100644 index 0000000000000000000000000000000000000000..8cfa5ed84a397f0e74283a476ba40fe0708d6df8 GIT binary patch literal 418133 zcmcfpWmuJM*9DB)C@m!df{28GfUxM266x-4>F!dHE|G5O1_2dmX#oLgkdl_}u00pe z^Sq{(jHqwVM64YiKyguj~DjUT5$kr-O*9gMziOgR`EU z(KSmw2OA4(2MbgE$4*9e_NLZWkC_?Z7yaWm4h}Y4jEw*H4Gh+HCXD`RgJ{>TJ-#L; zETHI;yf*3XVc?i@xjn*39Y-bQW}Tbj#PBQTy8pW>EZ-+I!TbbOYZ-w)^o50w*UHQJ zZ5?gzPm=w-QFx=;MS$T6yNc|60S2GIU)RE$zpx19u1n^wJLII8Mtw8w*X-eJ;1?A9 zlVkaZ%Cz4rHKLuT1rcZck!>mI@YsW2rxhXf9$WA(`PFa2RthF=*Z${k@3Hmuac2Mf z7Q8=y{hsareecfO_ZMrH$K>JvcTv(0*rNY?l_yxQ11bLZDv>Bp zTkikwRes+5|Gr(<(^6|=Lp_*LI>ayNl^75a|12?ht8LHt`m%r~& z;@PdW&zQAF`Ve6<9<#Wbp8G?i>jw`>^xRn4*>|^^v3CEMmtOxpIT<#+X)@OqHsP_` zO|MpNIA~e&eHK;Qb6<&;j!q8mb_nH(t&*(lkJ#rL-*-Cc;4|2=rTHVb*|@ByNBUy5 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zMnX?YTLIUX`-Fgj#0Iz`k!j zHCCoAKK)&3c2~sFA}_!4=>u*$O$8cc0;a~M!F^lD-z9~uoXtdyv2tbZ1A~eA>M>02 zxCj>sUq#X>I`HwU*c_AJa)I?t7b??zlq@!l7V6wxO`VU_4%4$R@pDzb)@iz#cEv%Z z)=B;Er5?W%E0Z0jrM>W`V+5OdT*|m9|^BcPdJ|%J8aN2D<-Z||4FPWd8fvY z>$4@bmD06&!vzm{bwx?jR8p9q-Oc0aUB@(yY<>;(zYFuF{q)daqa-ao;xMDgus)&I zf`pIJY0@-!-fTp|zdTEw7;s#~{Q&dK)3)spSi%wW4w56x?Y|XgAr=aT?!B()Y1qm9*mnmHqUHezG#ev=rpMtE1<`nwfSam zzLZ-BWHBX-kC8A-iQP}a(XF(H_Z+ek zQ#tdLFcRKZxZUd+Ro`g-7Ug^R^SD3}OdteL!IEhQ7%`ouE-J?@QjFCMm^$@Rh=cz4 zZaLEwacnz1;XaiqtyIbOEvfQYDMvMdKwOXOO~&wa-Xo*OekCnN{X5@m1|{hB!VUg^ zJv&PXkvJtu3c@6Myao*eA&Q<*SL$m<{ANyy{qWHNid^6rpCjP|Fg(VOKbR3a>%$=# zPTuqcH-9J(a|8~v?3yZ3T#(dMjFK6E|&@CVx48aju@n~1uYjO>wr3g~yTt+|A` zi!!QSLt$eM^4MPHS1K}sRF)VP4-bdNSE4OuTTVDxq zhF@!Oo}hx?%X$7x?e?1IqQSXJ!R3r+C$IDuf1%BK4YkM^R*kLKqTk^>_<#G)0JAy2 W_$5v2J{tIQ?8K2X#OE58*Z&I@z=bdX From 2aced6495fe5d9e2ab36be0056233a00711dc010 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Thu, 9 May 2019 10:00:50 +0200 Subject: [PATCH 33/65] updated datasets chapter begining --- text/hlavac-thesis.tex | 41 +++++++++++++++++++---------------------- 1 file changed, 19 insertions(+), 22 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 97dc498..7a47ea6 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -727,21 +727,14 @@ \subsection{$\delta$-Medoids} \chapter{Datasets}\label{ch:datasets} -This chapter explains the implementation and testing of selected methods in multiple different scenarios. -In the first section, algorithms for selected methods are listed and explained in detail. -The following section serves as a list of used datasets ranging from general to more specific ones. -The last section of this chapter talks about the tests that were run. -It also explains why the choice of these tests. - -\section{Datasets}\label{sec:datasets} -The task of cluster representation in non-metric spaces generally is not necessarily connected only to the Security field. -That is why the selected algorithms were tested on artificially created datasets, clustering datasets previously used in literature and on specific data collected from a real network. -Datasets that are needed for testing methods from this thesis have to be labeled. -That means that each sample in the dataset must be assigned into a cluster so that when the representation for each cluster is found and samples are assigned to clusters, it is possible to doublecheck if the assignment was right. -There are not many labeled datasets that have a lot of samples in each cluster and have the right features for testing. \todo{what does it mean right features, how to better write this} -That is why for complex testing datasets from picture recognition field were used for general testing of methods before going into the area of computer networks. - -\subsection{Datasets Created for this Work} +The task of cluster representation in non-metric spaces generally is not necessarily connected only to the security field. +That is why, before moving to a dataset collected from the real network, the selected algorithms were first tested on artificially created datasets and well labeled datasets found in the literature. \\ + +Below, the datasets used in this thesis are presented. +There are three types of datasets used: artificially created, image recognition and real network dataset. +Each section corresponds to one of the listed types. \\ \todo{is this introduction enough? What is missing?} + +\section{Datasets Created for this Work} These datasets were created to test the specific properties of tested methods. The datasets created are the following: @@ -761,11 +754,12 @@ \subsection{Datasets Created for this Work} Similarity measure used in these datasets was Euclidean distance. \\ The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter X. \todo{Link to chapter} The Overlap dataset consists of 3 clusters in shapes of blocks. -These blocks partly overlap to test how selected algorithms cope with overlapping clusters. Noisy Circles and Noisy Circles 3D are datasets are one of the hardest datasets to represent even by methods that are designed specifically for metric space. +These blocks partly overlap to test how selected algorithms cope with overlapping clusters. +Circles 3D is one of the hardest datasets to represent even by methods that are designed specifically for metric space. These two datasets were chosen to test the resistance to noise in selecting representatives. -There are two clusters as shown in figure X. \todo{add figure} +There are two clusters as shown in Figure~\ref{img:datasets}. -\subsection{Image Recognition Datasets} +\section{Image Recognition Datasets} One of the best-documented areas of clustering is the area of image recognition. That is why two datasets from this area were chosen for measuring the precision and complexity of each method. Both of them are labeled datasets of features collected from digits written by human hand. \\ @@ -776,12 +770,15 @@ \subsection{Image Recognition Datasets} Features represent coordinate information about the digits as they were written on a $500\times500$ pixel frame. This dataset is split to train dataset (70\%) and test dataset (30\%). -The Multiple Features Data Set \cite{dua1998pendigit} consist of \todo{explain how is it different} \todo{add MNIST} +The Multiple Features Data Set \cite{dua1998pendigit} consist of \todo{explain how is it different} \todo{add MNIST Fashion - if works remove multiple features??? Even though I have results.?} -\subsection{Real Network Dataset} -Real network traffic captured from a company with approximately 20k employees. +\section{Real Network Dataset} +Real network traffic captured from a company with approximately 20 thousand employees. The capture is from 24 hours of a working day a consists of \todo{count flows} flows. -This dataset was created from proxy logs in that company following the workflow explained in section x \todo{Link section.}. \\ +Proxy logs were used to capture the connections. +Hosts that appeared in that capture were clustered using the method explained in Section~\ref{sec:cluster_topo}. +These clusters with all hosts labeled were used as real network dataset in this thesis.\\ + %========================================CHAPTER================================== From 790720ee1e608468dc283f2c2db71f64da09a985 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Thu, 9 May 2019 10:14:59 +0200 Subject: [PATCH 34/65] merged chapters 2 and 3 --- text/hlavac-thesis.tex | 123 ++--------------------------------------- 1 file changed, 5 insertions(+), 118 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 7a47ea6..5a90a21 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -159,7 +159,7 @@ \chapter{Goals} %========================================CHAPTER================================== -\chapter{Intrusion Detection System} +\chapter{Intrusion Detection System}\label{ch:ids} This chapter focuses on presenting ideas from network security needed to understand the main objective of this thesis. Different approaches to network intrusion detection systems (IDS) are explained in Section~\ref{sec:ids}. @@ -306,121 +306,7 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} %========================================CHAPTER================================== -\chapter{Theoretical Overview} - -This chapter introduces a theoretical background to explain clustering methods, the topic of non-metric spaces and the problem of representative selection. - -\section{Introduction to Clustering}\label{sec:clust_intro} -As defined in \cite{guttag2016introduction}, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. -Without any prior knowledge of the data, this method looks for structures in feature vectors. -Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. -Variability and dissimilarity are two properties defined for a better understanding of the data. \\ -Variability is defined as $$ \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:,$$\todo{number equations} where \textit{e} is an object from given cluster. -The distance is a measure that quantifies how far two objects with the same features are. \todo{better wording here} -Many different distance measures are used in clustering. -Several of commonly used ones are: -\begin{itemize} - \item Euclidean distance - \item Minkowski distance - \item Manhattan distance - \item Cosine similarity -\end{itemize} - -Dissimilarity is defined as $$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:,$$ where $C$ stands for all clusters the data was clustered into. -For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. -Given this definition, the best way to cluster every dataset would be to put each object to its cluster. -That would not lead to any reasonable result. -Therefore there is a constraint added for clustering methods. -It can be either the maximum number of clusters or the maximum distance between two clusters. \\ - -A straightforward example of clustering is a method called agglomerative hierarchical clustering. -Given $N$ objects in a dataset, it creates $N$ clusters - meaning there is a cluster for each object. -The method looks for two closest clusters and merges them into one. -This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. -This method is a greedy algorithm, and therefore it might not result in globally optimal clustering. -Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$. -Therefore, it cannot be used in big datasets. \\ - -An example of a much faster clustering algorithm that is also greedy is $K$-means. \todo{How to cite that this is only taken from the book, even though rewritten and selected for my purposes.} -The '$K$' in $K$-means stands for the number of clusters that we want to get as a result. -To use this algorithm the number of desired clusters has to be known in advance. -$K$-means randomly chooses $K$ centroids in the space of the dataset and then assigns each point to a centroid. -After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. -The algorithm stops when the centroids of clusters stop changing. -Figure~\ref{alg:kmeans} shows the pseudocode of $K$-means. -\begin{algorithm}\label{alg:kmeans} - \caption{K-Means pseudocode} - \label{k_mean_pseudocode} - \begin{algorithmic}[1] - \INPUT set of examples $X$; number of clusters $k$ - \STATE Randomly chose $k$ examples from $X$ as centroids - \WHILE{True} - \STATE Create $k$ clusters by assigning each object to the closest centroid - \STATE Compute $k$ new centroids by averaging objects in each cluster - \IF{Centroids do not change} - \STATE \textbf{break} - \ENDIF - \ENDWHILE - \end{algorithmic} -\end{algorithm} \todo{better pseudocode, find better book} - -This algorithm is fast, it has time complexity $\mathcal{O}(k \cdot n)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. -It is the most common clustering algorithm as it typically converges in a few iterations. -For more details about basic clustering algorithms see \cite{guttag2016introduction}.\\ - -%summary -The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. -Firstly the number of clusters is not previously known, so choosing $K$ for $K$-means is not an option, and secondly, the distance measure used does not form a metric space. -Next section delves deeper into what it means when a measure does not form a metric space. - -\section{Clustering on Topological Space}\label{sec:cluster_topo} -This section explains the difference between metric and topological space and clustering approaches for topological spaces. \\ - -A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X \to \mathbb{R}$\todo{ad pripominka, ze by to melo byt NxN, takhle to je popsane v te citovane knize. Prijde mi, ze je to obecnejsi a drzim se toho jak to popsal ten matematik.} which satisfies the following conditions: -\begin{enumerate} - \item [(i)] $d(x, y) \geq 0$; - \item [(ii)] $d(x, y) = 0 \iff x = y$; - \item [(iii)] $d(x, y) = d(y, x)$ - \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \mathrm{for} x, y, z \in X$. -\end{enumerate} - -The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. -This is why this similarity measure forms a topological space, which is defined according to, e.g. \cite{stahl2014introduction}. \\ - -Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: -\begin{enumerate} - \item [(i)] The empty set $\emptyset$ is in $F$. - \item [(ii)] The given set $S$ is in $F$. - \item [(iii)] The intersection of any two sets of $F$ is in $F$. - \item [(iv)] The union of any number of sets of $F$ is in $F$. -\end{enumerate} -The ordered pair ($S, F$) is called a topological space. \\ - -\todo{explain about neighborhoods} - -K-Means clustering from the previous section cannot be used in topological space as the centroids do not exist. -In many cases, there is no prior knowledge of k, as is the case in this thesis. -A popular method that is used for clustering in non-metric spaces is called community-based clustering. \\ - -Community-based clustering detects communities in the data. -A community is a subset of examples in the data, that is densely connected with each other. -One of the ways how to detect communities in a graph is to create a full-adjacency matrix. -This matrix contains all connections between all nodes in the given graph. -Analyzing the matrix then can tell us about the densities in different parts of graphs.\\ - -Louvain method is a similar approach to clustering when we do not have a simple graph but a set of samples and a pairwise similarity measure. -This method relies on creating a full similarity matrix for the whole dataset and then looking for communities in the data. \\ - -In Cognitive Targeted Anomaly Detection Framework Louvain method cannot be used directly as calculating the full similarity matrix has a complexity of $\mathcal(n^2)$, which is impossible to calculate for a large network. -That is why an approximative clustering method is used. -This method iteratively samples network hosts and runs the clustering algorithm on the sampled hosts. -Each iteration creates or updates cluster prototypes. -If the data in the current batch fit into a previously prototyped cluster, they are added to it and the cluster prototype is updated. -If samples differ more than a predefined threshold a new cluster prototype is created. - -%========================================CHAPTER================================== - -\chapter{Representative Selection} +\chapter{Theoretical Overview}\label{ch:theory} This chapter introduces a theoretical background to explain clustering methods, the topic of non-metric spaces and the problem of representative selection. @@ -721,7 +607,8 @@ \subsection{$\delta$-Medoids} \ENDFOR \DOWHILE{$representatives_t = representatives_{t-1}$} \end{algorithmic} -\end{algorithm} \todo{add the hack for better results} +\end{algorithm} +\todo{add my improvement to the pseudocode} %========================================CHAPTER================================== @@ -782,7 +669,7 @@ \section{Real Network Dataset} %========================================CHAPTER================================== -\chapter{Experiments} +\chapter{Experiments}\label{ch:experiments} In this chapter each experiment that was run. Each section is dedicated to one experiment. For each experiment, the motivation, experimental setup, results and conclusion are listed. From 1f37a720550f4a451bf374029747c6552b73243f Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Thu, 9 May 2019 10:19:19 +0200 Subject: [PATCH 35/65] added structure to introduction --- text/hlavac-thesis.tex | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 5a90a21..3d47076 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -110,7 +110,10 @@ \chapter{Introduction} Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ -\todo{Chapters and sections will be briefly explained here.} +The thesis is organized as follows. Chapter~\ref{ch:ids} anomaly-based network security. +In Chapter~\ref{ch:theory}, the theory behind clustering and representative selection in metric and non-metric spaces is explained. +Datasets used in for testing are presented in Chapter~\ref{ch:datasets}. +Chapter~\ref{ch:experiments} covers all the experiments with their results and is followed by the conclusion of this thesis. %The main focus of this work is in representing these clusters found by clustering algorithms. %Finding representation of clusters in Cognitive targeted anomaly detection framework is made more difficult by the fact, that similarity measure used for clustering does not form a metric space. From 96c149de313340455ce733a18608298a81f8a981 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Thu, 9 May 2019 10:39:09 +0200 Subject: [PATCH 36/65] added citation, numbered equations --- text/hlavac-thesis.bib | 14 +++++++++++++- text/hlavac-thesis.tex | 24 ++++++++++++++++++------ 2 files changed, 31 insertions(+), 7 deletions(-) diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib index 0affa90..c5ee469 100644 --- a/text/hlavac-thesis.bib +++ b/text/hlavac-thesis.bib @@ -85,4 +85,16 @@ @misc{dua1998pendigit url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } - +@techreport{rfc2722, + AUTHOR = "Brownlee, N. and Mills, C. and Ruth, G.", + TITLE = "{Traffic Flow Measurement: Architecture}", + HOWPUBLISHED = {Internet Requests for Comments}, + TYPE="{RFC}", + NUMBER=2722, + YEAR = {1999}, + MONTH = {October}, + ISSN = {2070-1721}, + PUBLISHER = "{RFC Editor}", + INSTITUTION = "{RFC Editor}", + URL={https://www.rfc-editor.org/rfc/rfc2722.txt} +} diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 3d47076..4a805c8 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -210,8 +210,7 @@ \section{Network Anomaly Detection}\label{sec:nbad} Host behavior is collected from devices in the network. There is at least one device (although many times multiple) dedicated for collection of network flows (using NetFlow protocol). Flow collection is the most widely used method for gathering data on the network. -One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and size of data transferred in each direction. -\todo{[RFC2722] citation} +One network flow is an aggregated information about one connection that consists of source and destination addresses, source, and destination ports, begin and end timestamps for communication and size of data transferred in each direction \cite{rfc2722}. This aggregation of information enables much faster (even real-time) detection of problems on a network as compared to, for example, deep packet inspection (DPI). DPI is another method used to detect problems in a network. It focuses on exploring the payload of each packet and is mostly considered unusable. @@ -276,7 +275,10 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} \item $F^h$ represents the frequency of visits of server:port pairs \end{itemize} The frequency is defined as: -$$F_s^h = \frac{i}{n}\sum\limits_{1}{n}I(t_i, s, h)\:,$$ where $n$ is the number of time windows, $I$ is the indicator function, which is 1 if the network host $h$ visited the server $s$ in the timewindow $t_i$ and 0 otherwise. +\begin{equation} +F_s^h = \frac{i}{n}\sum\limits_{1}{n}I(t_i, s, h)\:, +\end{equation} +where $n$ is the number of time windows, $I$ is the indicator function, which is 1 if the network host $h$ visited the server $s$ in the timewindow $t_i$ and 0 otherwise. This ratio-based frequency ensures that frequently visited servers (e.g., Google, Facebook) do not overshadow the less frequently visited servers.\\ When each host is represented, the clustering algorithm is started. @@ -287,7 +289,9 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: -$$\textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:,$$ +\begin{equation} +\textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:, +\end{equation} where $F_a$, $F_b$ represent the frequency and $S$ represents the union of sets of servers visited by the network hosts $a$ and $b$. \\ After the clustering is done, only clusters that are bigger than 10 hosts are selected for representative selection. @@ -318,7 +322,11 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Without any prior knowledge of the data, this method looks for structures in feature vectors. Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. Variability and dissimilarity are two properties defined for a better understanding of the data. \\ -Variability is defined as $$ \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:,$$\todo{number equations} where \textit{e} is an object from given cluster. +Variability is defined as +\begin{equation} +\mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:, +\end{equation} +\todo{are these equation numbers-corresponding to sections correct?} where \textit{e} is an object from given cluster. The distance is a measure that quantifies how far two objects with the same features are. \todo{better wording here} Many different distance measures are used in clustering. Several of commonly used ones are: @@ -329,7 +337,11 @@ \section{Introduction to Clustering}\label{sec:clust_intro} \item Cosine similarity \end{itemize} -Dissimilarity is defined as $$ \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:,$$ where $C$ stands for all clusters the data was clustered into. +Dissimilarity is defined as +\begin{equation} +\mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:, +\end{equation} + where $C$ stands for all clusters the data was clustered into. For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. Given this definition, the best way to cluster every dataset would be to put each object to its cluster. That would not lead to any reasonable result. From 424892ea8244841f6383f6bd58844511a2ee902d Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Thu, 9 May 2019 12:21:04 +0200 Subject: [PATCH 37/65] fixed few todo notes and added refs --- text/hlavac-thesis.bib | 9 ++++++++- text/hlavac-thesis.tex | 14 +++++++------- 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib index c5ee469..8fd4373 100644 --- a/text/hlavac-thesis.bib +++ b/text/hlavac-thesis.bib @@ -63,7 +63,14 @@ @book{choudhary1992elements year={1992}, publisher={New Age International}, pages={20--20}, - ISBN={978-81-224-0399} + ISBN={978812240399} +} +@book{tan2014introduction, + title={Introduction to data mining}, + author={Tan, Pang-Ning and Steinbach, Michael and Kumar, Vipin}, + year={2014}, + publisher={Harlow: Pearson}, + ISBN={9781292026152} } @book{stahl2014introduction, title={Introduction to topology and geometry}, diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 4a805c8..ca85271 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -356,7 +356,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$. Therefore, it cannot be used in big datasets. \\ -An example of a much faster clustering algorithm that is also greedy is $K$-means. \todo{How to cite that this is only taken from the book, even though rewritten and selected for my purposes.} +An example of a much faster clustering algorithm that is also greedy is $K$-means. The '$K$' in $K$-means stands for the number of clusters that we want to get as a result. To use this algorithm the number of desired clusters has to be known in advance. $K$-means randomly chooses $K$ centroids in the space of the dataset and then assigns each point to a centroid. @@ -399,7 +399,7 @@ \section{Clustering on Topological Space}\label{sec:cluster_topo} \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \mathrm{for} x, y, z \in X$. \end{enumerate} -The similarity measure that is used in this thesis as explained in section 1.4 \todo{reference} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. +The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctdaf} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. \\ Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: @@ -441,7 +441,7 @@ \section{Representative Selection} If it was possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. \\ A cluster prototype is a data sample that represents all samples in the data cluster. -According to \todo{ref Introduction to Data Mining}, there are three motivations for finding the most representative cluster prototypes: +According to \cite{tan2014introduction}, there are three motivations for finding the most representative cluster prototypes: \begin{itemize} \item Summarization \item Compression @@ -481,7 +481,7 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_metric} \todo{Centroid by si mel zavest a vysvetlit vyhody a nevyhody. Centroid muze byt vazeny nebo aritmeticky prumer, to dokazes zapsat vzorcem a bude to hezky vypadat nebo obrazek nebo tak neco. Medoid si vubec nevysvetlil co je.} -A dataset that has the same number of features for each sample and a metric that fulfills all the requisites shown in \todo{reference the definition} is an example of a dataset in a metric space. +A metric dataset has to have the same number of features for each sample and a metric that fulfills all the requisites in the definition of metric space. Selecting a prototype from this dataset is often best achieved by calculating a centroid for a given cluster. A centroid can be, for example, the average of all points in the cluster. If such a centroid is meaningful (it does not fall away from the data), it can be selected as a prototype. @@ -511,8 +511,8 @@ \section{Algorithms Relevant for Topological Space}\label{sec:relevant_methods} \begin{itemize} \item Random selection \item Greedy Selection - \item Delta-Medoids One-Shot - \item Delta-Medoids \todo{citations} + \item $\delta$-Medoids One-Shot + \item $\delta$-Medoids \end{itemize} \subsection{Random Selection}\label{subsec:random_select} @@ -531,7 +531,7 @@ \subsection{Greedy Selection}\label{subsec:greedy_select} The greedy selection algorithm is a straightforward algorithm that selects a random sample from the set as the first representative. Then it looks for the most different sample in the rest of the dataset. Once the desired sample is found, the algorithm removes all neighboring samples with at least the similarity of $\delta$ from the set. -Then it looks for other dissimilar samples until the whole set is represented. \\ \todo{pseudocode} +Then it looks for other dissimilar samples until the whole set is represented. \\ The speed of this algorithm depends on the sparsity of the given set. In the worst case scenario, all points would be selected as representatives while always calculating the distance to each remaining sample in the set. Then it would reach the time complexity of $\mathcal{O}(n^2)$. From b0803bf94145596d4107ecc9d3553cd2fd3ab9fc Mon Sep 17 00:00:00 2001 From: Vaclav Hlavac Date: Thu, 9 May 2019 16:59:39 +0200 Subject: [PATCH 38/65] VH in intro --- text/hlavac-thesis.tex | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 4a805c8..f9e77b7 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -85,11 +85,16 @@ \chapter{Introduction} Multi-layer defense systems are protecting contemporary networks. As attackers overcome one layer, there is yet another layer waiting for them. -However, if the attackers succeed to bypass all of them unnoticed, a huge problem can occur. -They can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. +However, if the attackers succeed to bypass all +%VH of them +{\cbl security layers} +unnoticed, a huge problem can occur. +%VH They +{\cbl Attackers} can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. It can take a long time before somebody notices the breach. A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. -Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. \\ +Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. +%VH \\ One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). In NBAD, devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. @@ -990,8 +995,9 @@ \chapter{Conclusion} \chapter{Abreviations} % \printglossaries \begin{description} - \item[NIDS] Intrusion Detection System +%VH I changed the order of NBAD and NIDS to keep alphabetical order \item[NBAD] Network-Based Anomaly Detection + \item[NIDS] Intrusion Detection System \end{description} From 76fda61fc0726c75c6014b8e1f7c0c7b54c8c37d Mon Sep 17 00:00:00 2001 From: Vaclav Hlavac Date: Thu, 9 May 2019 17:12:25 +0200 Subject: [PATCH 39/65] Intro finished. --- text/hlavac-thesis.tex | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index f9e77b7..e563353 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -107,17 +107,23 @@ \chapter{Introduction} In order to detect such anomalies, it is needed to focus on smaller parts of the network. Behavioral clustering of network hosts can be used to separate it into smaller pieces. \\ -In Cognitive targeted anomaly detection framework from Cisco behavioral clustering is used to find groups of similar hosts in a network. +In Cognitive targeted anomaly detection framework from Cisco, +%VH comma added +behavioral clustering is used to find groups of similar hosts in a network. Each cluster created consists of hosts that have similar behavior. The main focus of this work is to find a representation of these clusters that are used to calculate the model behavior. -These cluster prototypes\todo{Can I use the term cluster prototype before first explaining it?} are then updated dynamically as the behavior of the network changes. \\ +%VH +{\cbl The representation used in this thesis, is based on cluster prototypes, which will be described in the sequel.} +These cluster prototypes\todo{Can I use the term cluster prototype before first explaining it? VH: I think I solved it.} are then updated dynamically as the behavior of the network changes. \\ Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ -The thesis is organized as follows. Chapter~\ref{ch:ids} anomaly-based network security. -In Chapter~\ref{ch:theory}, the theory behind clustering and representative selection in metric and non-metric spaces is explained. -Datasets used in for testing are presented in Chapter~\ref{ch:datasets}. +The thesis is organized as follows. +{\cbl +Chapter~\ref{ch:ids} {\cbl introduces the} anomaly-based network security. +Chapter~\ref{ch:theory} explains the theory behind clustering and representative selection in metric and non-metric spaces. +Chapter~\ref{ch:datasets} presents datasets used for testing.} Chapter~\ref{ch:experiments} covers all the experiments with their results and is followed by the conclusion of this thesis. %The main focus of this work is in representing these clusters found by clustering algorithms. From 267c356c111cd9581d1900ee00ade6336606d115 Mon Sep 17 00:00:00 2001 From: Vaclav Hlavac Date: Thu, 9 May 2019 17:13:42 +0200 Subject: [PATCH 40/65] Jeste jedna carka ve vete chybne. Byla to moje chyba. --- text/hlavac-thesis.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index e563353..ccf3d5f 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -113,7 +113,7 @@ \chapter{Introduction} Each cluster created consists of hosts that have similar behavior. The main focus of this work is to find a representation of these clusters that are used to calculate the model behavior. %VH -{\cbl The representation used in this thesis, is based on cluster prototypes, which will be described in the sequel.} +{\cbl The representation used in this thesis is based on cluster prototypes, which will be described in the sequel.} These cluster prototypes\todo{Can I use the term cluster prototype before first explaining it? VH: I think I solved it.} are then updated dynamically as the behavior of the network changes. \\ Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. From 5d254d6a7d84d61e45690aa91ce057685e5a8983 Mon Sep 17 00:00:00 2001 From: Vaclav Hlavac Date: Fri, 10 May 2019 03:51:44 +0200 Subject: [PATCH 41/65] Kapitola Goals --- text/hlavac-thesis.tex | 43 +++++++++++++++++++++++++++++------------- 1 file changed, 30 insertions(+), 13 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index ccf3d5f..ffa3bfc 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -85,15 +85,15 @@ \chapter{Introduction} Multi-layer defense systems are protecting contemporary networks. As attackers overcome one layer, there is yet another layer waiting for them. -However, if the attackers succeed to bypass all -%VH of them +However, if the attackers succeed to bypass all +%VH of them {\cbl security layers} unnoticed, a huge problem can occur. -%VH They +%VH They {\cbl Attackers} can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. It can take a long time before somebody notices the breach. A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. -Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. +Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. %VH \\ One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). @@ -113,13 +113,13 @@ \chapter{Introduction} Each cluster created consists of hosts that have similar behavior. The main focus of this work is to find a representation of these clusters that are used to calculate the model behavior. %VH -{\cbl The representation used in this thesis is based on cluster prototypes, which will be described in the sequel.} +{\cbl The representation used in this thesis is based on cluster prototypes, which will be described in the sequel.} These cluster prototypes\todo{Can I use the term cluster prototype before first explaining it? VH: I think I solved it.} are then updated dynamically as the behavior of the network changes. \\ Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ -The thesis is organized as follows. +The thesis is organized as follows. {\cbl Chapter~\ref{ch:ids} {\cbl introduces the} anomaly-based network security. Chapter~\ref{ch:theory} explains the theory behind clustering and representative selection in metric and non-metric spaces. @@ -151,22 +151,39 @@ \chapter{Introduction} \chapter{Goals} -This work aims to study methods used for the representation of network host behavioral clusters that can be used in Cognitive targeted anomaly detection framework. -Behavioral similarity used for clustering in this framework does not form a metric space, which creates a requirement for algorithms used for representative selection. \\ +This work aims to study methods used for the representation of network host behavioral clusters that can be +%VH used; not to repeat the same word +{\cbl applied} in Cognitive targeted anomaly detection framework. +Behavioral similarity used for clustering in this framework does not form a metric space, which creates a requirement for algorithms used for representative selection. {\cbl Typesetting. See \%VH comment.} +%VH \\ This is not a good typesetting approach. Use \smallskip or \medskip or \bigskip +%VH Even the solution above is not the best one. Changing the style would be the way. +%VH My advice: Follow the style and do not put extra space after paragraphs. +\medskip This thesis should present a method that can be used for finding the cluster prototypes of clusters in non-metric space. These cluster prototypes represent the whole cluster in a way that is convenient for further computation. -It can be, for example, assigning newly added samples to their corresponding cluster.\\ +It can be, for example, assigning newly added samples to their corresponding cluster. +%VH\\ +\medskip Networks monitored by Cognitive targeted anomaly detection framework can have up to several hundreds of thousands of hosts which leads to massive clusters. If the classification of newly added hosts were done by comparing the new host to all others, the memory and time needed for classification would be unmanageable. -That is why the number of representatives selected from each cluster should be kept as small as possible. \\ +That is why the number of representatives selected from each cluster should be kept as small as possible. +%VH \\ +\medskip General methods used for general cluster representation in non-metric spaces should be studied. From these previously studied methods, the best ones will be selected and implemented. -As part of this thesis, there should also be created a dataset as a benchmark for testing the chosen method. -A further goal is to test the chosen methods on generally used clustering datasets and the created benchmark dataset. -Based on this testing, find the method that suits best for use in this field and incorporates it into the Cognitive targeted anomaly detection framework. +As part of this thesis, +%VH there should also be created a dataset as a benchmark for testing the chosen method. +%VH Word order corrected +{\cbl a benchmark dataset for testing the chosen method should also be created.} +A further goal is to test the chosen methods +%VH on generally used clustering datasets and the created benchmark dataset. +{\cbl on clustering datasets used by others and on a newly created benchmark dataset.} +Based on this testing, +%VH find +{\cbl the method should be found} that suits best for use in this field and incorporates it into the Cognitive targeted anomaly detection framework. The selected method should be finetuned to get the best possible results in anomaly-detection. \setsecnumdepth{all} From f35dcb003b8eb88d7d611a6b8fdd12ccb50382a1 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 10 May 2019 08:28:20 +0200 Subject: [PATCH 42/65] getting the repo up to date --- jupyter/bc_utils.py | 322 +++- jupyter/data/pendigit.csv | 3498 ----------------------------------- jupyter/make_datasets.ipynb | 55 +- jupyter/testbed.ipynb | 791 +++++++- text/hlavac-thesis.tex | 52 +- 5 files changed, 1132 insertions(+), 3586 deletions(-) delete mode 100644 jupyter/data/pendigit.csv diff --git a/jupyter/bc_utils.py b/jupyter/bc_utils.py index fbcdcac..d469301 100644 --- a/jupyter/bc_utils.py +++ b/jupyter/bc_utils.py @@ -160,6 +160,74 @@ def find_best_cluster_representative(cluster, similarity_measure): return cluster[best_repr_index] def delta_medoids_full(df, delta, similarity_measure): + + t = 0 #iteration number + representatives = {} #selected representatives + representatives[t] = set() #representatives for given iteration + size_threshold = 0.01 * len(df.index) + + while True: + #print("\n=========== running for t = " + str(t) + "============") + clusters = {} #subclusters inside cluster + for item in representatives[t]: + clusters[item] = np.array(item, ndmin=2) + + t = t + 1 + + #================== RepAssign starts ================== + for row in df.iterrows(): + dist = float("inf") + represenative = None + + point = tuple(row[1]) + + for rep in clusters.keys(): + #finding the closest representative to current point + if similarity_measure(point, rep) <= dist: + representative = rep + dist = similarity_measure(point, rep) + if dist <= delta: + clusters[representative] = np.vstack((clusters[representative], point)) + elif t == 1: + clusters[point] = np.array(point, ndmin=2) + #================== RepAssign ends =================== + + + if (len(clusters.keys()) > 50) or (len(clusters.keys()) > len(df.index) * 0.1): + for key in clusters.keys(): + if len(clusters[key]) <= size_threshold: + tmp_sim = 0 + max_sim = 0 + new_key = key + for key_sim in clusters.keys(): + tmp_sim = similarity_measure(key, key_sim) + if key_sim == key: + continue + elif tmp_sim > max_sim: + new_key = key_sim + max_sim = tmp_sim + +# if new_key == key: +# print('Dropping this cluster, it is not relevant.') + if new_key != key: #else: + clusters[new_key] = np.vstack((clusters[new_key], clusters[key])) + del(clusters[key]) +# print 'clusters remaining', len(clusters.keys()) + + representatives[t] = set() + for cluster in clusters.values(): + representative = find_best_cluster_representative(cluster, similarity_measure) + #print(representative) + representatives[t].add(tuple(representative)) + #print(representatives[t]) + + if representatives[t] == representatives[t-1]: + break + + #print("delta_medoids_full algorithm ended after " + str(t) + " iterations.") + return pd.DataFrame(list(representatives[t]), columns=df.columns.values) + +def delta_medoids_full_old(df, delta, similarity_measure): """Returns subset of input DataFrame, that is a good representation of given data. @@ -321,6 +389,42 @@ def greedy_select(df, delta, similarity_measure): return df +def greedy_select_real(df, delta, similarity_measure): + + representatives = np.array([], str) # selected representatives + remaining = np.array(df.index.values, str) # remaining indexes not covered + + while (remaining.size > 0): + if (representatives.size == 0): # selecting the first index + current_index = df.loc[remaining[0]].name + else: # finding index of the most different point + current_index = None + sim_sum = representatives.size # used to store the biggest sum + for index in remaining: + tmp_sum = 0 # used to store the current sum + for medoid in representatives: # finding different the point is + tmp_sum += similarity_measure(df.loc[index]['data'], df.loc[medoid]['data']) + if tmp_sum < sim_sum: + sim_sum = tmp_sum + current_index = index + + # adding selected index to other representatives + representatives = np.append(representatives, current_index) + + # find indexes to drop from dataframe + rem_indexes = [i for i in remaining if similarity_measure(df.loc[i]['data'], df.loc[current_index]['data']) >= delta] + + rem_indexes = np.array([], str) + for ix in remaining: + if similarity_measure(df.loc[ix]['data'], df.loc[current_index]['data']) >= delta: + rem_indexes = np.append(rem_indexes, ix) + + # dropping points from the delta distance of newly selected representative + remaining = np.array([item for item in remaining if item not in rem_indexes]) + #TODO DELETE? rem_indexes = np.delete(rem_indexes, np.where(rem_indexes == current_index)) + + return df.loc[representatives] + def classifyPoints(ref_df, test_df): X_train = ref_df.iloc[:, :-1].values @@ -344,9 +448,58 @@ def classifyPoints(ref_df, test_df): from sklearn.metrics import classification_report, confusion_matrix conf_mat = confusion_matrix(y_test, y_pred) #print(conf_mat) - #print(classification_report(y_test, y_pred)) + print(classification_report(y_test, y_pred)) return conf_mat +def classifyPoints_real(ref_df, test_df, similarity): + + names = set(ref_df['cluster']) + + belongs = {} + for name in names: + belongs[name] = {} + for n in names: + belongs[name][n] = 0 + + suc = 0 + fai = 0 + miss = 0 + for row in test_df.iterrows(): + sim = 0 + cluster = 'NONE' + for ref in ref_df.iterrows(): + tmp_sim = similarity(row[1]['data'], ref[1]['data']) + if tmp_sim > sim: + sim = tmp_sim + cluster = ref[1]['cluster'] + if sim == 1: + break + + if(sim == 0): + miss += 1 + continue + else: + belongs[row[1]['cluster']][cluster] += 1 + + if cluster == row[1]['cluster']: + suc += 1 + else: + fai += 1 + + print('Missed: ' + str(miss)) + print('Succeeded: ' + str(suc)) + print('Failed: ' + str(fai)) + print('Ratio: ' + str(float(suc) / float(suc + fai))) + + names = sorted(names) + matrix = [[0 for i in names] for i in names] + + for i in range(0, len(names)): + for j in range(0, len(names)): + matrix[i][j] = belongs[names[i]][names[j]] + + return np.array(matrix) + #input is a precision recall matrix and this methore calculates the ratio #it is used for comparison of algorithms def get_hit_miss_rate(matrix): @@ -366,6 +519,87 @@ def get_hit_miss_rate(matrix): return float(miss)/float(hit) +def get_method_results_updated(full_test_df, full_train_df, similarity, delta): + test_res = {"delta_medoids_full" : {}, + "delta_medoids_one_shot" : {}, + "random_select" : {}, + "greedy_select" : {}} + + #creating training DataFrames for comparing oneshot and full delta medoids algorithm + train_delta_medoids_full = pd.DataFrame() + train_delta_medoids_one_shot = pd.DataFrame() + train_random_selection = pd.DataFrame() +# train_greedy_select = pd.DataFrame() + + for name in set(full_train_df['cluster']): + delta_df = full_train_df[full_train_df['cluster'] == name].iloc[:, :-1] + print('Running for :' + str(name)) + print(len(delta_df.index)) + + if delta == None: #estimating delta if none is set + delta = estimate_delta(delta_df, similarity) + + #delta medoids full + print('Doing delta-medoids Full') + medoids_full_result = delta_medoids_full(delta_df, delta, similarity) + medoids_full_result['cluster'] = name #setting a cluster name for result + test_res["delta_medoids_full"][name] = medoids_full_result + train_delta_medoids_full = train_delta_medoids_full.append(medoids_full_result) + + print('Doing delta-medoids ONE SHOT') + #delta medoids one shot + one_shot_medoids_result = delta_medoids_one_shot(delta_df, delta, similarity) + one_shot_medoids_result['cluster'] = name + test_res["delta_medoids_one_shot"][name] = one_shot_medoids_result + train_delta_medoids_one_shot = train_delta_medoids_one_shot.append(one_shot_medoids_result) + + print('Doing Random Select') + #random select + random_select_result = random_select(delta_df, medoids_full_result.shape[0]) + random_select_result['cluster'] = name + test_res["random_select"][name] = random_select_result + train_random_selection = train_random_selection.append(random_select_result) + + #greedy select +# greedy_select_result = greedy_select(delta_df, delta, similarity) +# greedy_select_result['cluster'] = name +# test_res["greedy_select"][name] = greedy_select_result +# train_greedy_select = train_greedy_select.append(greedy_select_result) + + names = set(full_test_df['cluster']) + full = list() + greedy = [] + one_shot = [] + delta_med = [] + random = [] + for name in names: + full.append(len(full_train_df[full_train_df['cluster'] == name].index)) + greedy.append(0)#len(train_greedy_select[train_greedy_select['cluster'] == name].index)) + one_shot.append(len(train_delta_medoids_one_shot[train_delta_medoids_one_shot['cluster'] == name].index)) + delta_med.append(len(train_delta_medoids_full[train_delta_medoids_full['cluster'] == name].index)) + random.append(len(train_random_selection[train_random_selection['cluster'] == name].index)) + + + titles = ['Cluster', 'Full', 'Greedy', 'One Shot', 'Delta Medoids', 'Random Select'] + data = [titles] + list(zip(names, full, greedy, one_shot, delta_med, random)) + + for i, d in enumerate(data): + line = '|'.join(str(x).ljust(12) for x in d) + print(line) + if i == 0: + print('-' * len(line)) + + print('Full Medoids') + matrix_full = classifyPoints(train_delta_medoids_full, full_test_df) + print('Delta Medoids') + matrix_one_shot = classifyPoints(train_delta_medoids_one_shot, full_test_df) + print('Random Selection') + matrix_random_selection = classifyPoints(train_random_selection, full_test_df) +# print('Greedy Selection') +# matrix_greedy_select = classifyPoints(train_greedy_select, full_test_df) + return [matrix_full, matrix_one_shot, matrix_random_selection] # matrix_greedy_select] + + def get_method_results(full_test_df, dataframes, similarity, delta): test_res = {"delta_medoids_full" : {}, "delta_medoids_one_shot" : {}, @@ -408,10 +642,96 @@ def get_method_results(full_test_df, dataframes, similarity, delta): test_res["greedy_select"][name] = greedy_select_result train_greedy_select = train_greedy_select.append(greedy_select_result) + names = set(full_test_df['cluster']) + full = list() + greedy = [] + one_shot = [] + delta_med = [] + random = [] + for name in names: + full.append(4 * len(full_test_df[full_test_df['cluster'] == name].index)) + greedy.append(len(train_greedy_select[train_greedy_select['cluster'] == name].index)) + one_shot.append(len(train_delta_medoids_one_shot[train_delta_medoids_one_shot['cluster'] == name].index)) + delta_med.append(len(train_delta_medoids_full[train_delta_medoids_full['cluster'] == name].index)) + random.append(len(train_random_selection[train_random_selection['cluster'] == name].index)) + + + titles = ['Cluster', 'Full', 'Greedy', 'One Shot', 'Delta Medoids', 'Random Select'] + data = [titles] + list(zip(names, full, greedy, one_shot, delta_med, random)) + + for i, d in enumerate(data): + line = '|'.join(str(x).ljust(12) for x in d) + print(line) + if i == 0: + print('-' * len(line)) + + print('Full Medoids') matrix_full = classifyPoints(train_delta_medoids_full, full_test_df) + print('Delta Medoids') matrix_one_shot = classifyPoints(train_delta_medoids_one_shot, full_test_df) + print('Random Selection') matrix_random_selection = classifyPoints(train_random_selection, full_test_df) + print('Greedy Selection') matrix_greedy_select = classifyPoints(train_greedy_select, full_test_df) return [matrix_full, matrix_one_shot, matrix_random_selection, matrix_greedy_select] #TODO return also sizes of selections to evaluate how many of the dataset percentagewise it is + +#methods for real data + +#this method will take one 5min timewindow file +# and update the data structure based on it +def read_batch_file(path, data): + data_file = pd.read_csv(path, sep='\t') + #structure to load information from file into + # {user1:{host1:freq, host2:freq}, user2:{host1:freq, host2:freq}} + if data == None: + data = {} + + for index, row in data_file.iterrows(): + #extract information from hostnamePort + raw_hostnames = row['hostnamePort'].split(';') + user_id = row['userID'] + + #update tables of userIDs + if user_id not in data: + hostnames = {} + for host in raw_hostnames: + hostnames[host] = 1 + + data[user_id] = hostnames + else: + for host in raw_hostnames: + if host in data[user_id]: + data[user_id][host] += 1 + else: + data[user_id][host] = 1 + return data + +# similarity from paper +def kopp_similarity(host_a, host_b): + timewindow_num = 24.0 + common = list(set(host_a.keys() + host_b.keys())) + + #calculating similarity + if len(common) == 0: + res = 0 + else: + a = 0.0 + b = 0.0 + c = 0.0 + + for host in common: + f_a = float(host_a.get(host, 0)) / timewindow_num + f_b = float(host_b.get(host, 0)) / timewindow_num + + a += float(f_a) * float(f_b) + b += float(f_a)**2 + c += float(f_b)**2 + + if a == 0.0: + res = 0.0 + else: + res = float(a) / (np.sqrt(float(b)) * np.sqrt(float(c))) + + return res \ No newline at end of file diff --git a/jupyter/data/pendigit.csv b/jupyter/data/pendigit.csv deleted file mode 100644 index d1c3939..0000000 --- a/jupyter/data/pendigit.csv +++ /dev/null @@ -1,3498 +0,0 @@ -,a,b,c,d,e,f,g,h,i,j,l,m,n,o,p,q,cluster -0,80,100,18,98,60,66,100,29,42,0,0,23,42,61,56,98,8 -1,0,94,9,57,20,19,7,0,20,36,70,68,100,100,18,92,8 -2,95,82,71,100,27,77,77,73,100,80,93,42,56,13,0,0,9 -3,68,100,6,88,47,75,87,82,85,56,100,29,75,6,0,0,9 -4,70,100,100,97,70,81,45,65,30,49,20,33,0,16,0,0,1 -5,40,100,0,81,15,58,100,57,47,87,50,88,40,42,36,0,4 -6,3,71,0,95,45,100,100,99,79,78,48,53,31,24,54,0,7 -7,79,87,98,81,71,100,72,73,100,66,91,21,48,0,0,13,9 -8,92,95,30,100,34,68,87,89,84,78,100,35,64,0,0,19,9 -9,58,64,100,96,27,100,0,63,79,65,91,72,48,36,10,0,9 -10,34,89,3,70,1,25,49,0,100,23,100,67,56,99,0,100,0 -11,0,90,46,100,88,92,79,69,60,48,39,27,47,6,100,0,2 -12,20,71,0,29,31,0,78,12,100,51,84,93,37,100,8,66,0 -13,100,100,67,98,41,80,44,50,78,42,68,16,35,2,0,0,5 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-1832,100,100,60,100,36,91,34,62,68,56,70,33,38,14,0,0,5 -1833,19,92,0,57,64,39,64,0,5,11,13,62,30,100,100,99,5 -1834,0,64,16,100,47,79,35,40,6,6,11,9,58,0,100,4,2 -1835,38,100,15,59,18,13,63,0,100,33,96,77,48,90,0,72,0 -1836,11,80,0,34,31,0,77,17,100,60,72,100,24,94,3,51,0 -1837,66,100,41,77,0,49,18,29,77,30,100,63,90,30,95,0,4 -1838,0,92,50,100,70,75,25,53,58,42,100,20,58,0,3,1,3 -1839,100,99,62,100,26,78,2,48,0,16,38,0,59,23,20,38,6 -1840,45,96,23,78,0,35,29,0,83,21,100,65,63,100,3,91,0 -1841,42,100,24,72,0,43,50,49,93,70,100,61,94,29,99,0,4 -1842,54,92,0,100,16,65,66,35,72,0,22,15,56,51,100,84,8 -1843,75,94,95,90,62,100,84,86,100,52,91,16,44,0,0,18,9 -1844,0,85,51,100,81,71,76,35,58,0,31,25,35,55,100,61,7 -1845,0,86,39,100,89,95,73,74,70,52,100,33,100,13,50,0,3 -1846,18,82,0,38,23,0,70,6,100,41,85,83,39,100,4,74,0 -1847,0,79,61,54,100,21,61,0,43,34,82,69,82,100,0,92,8 -1848,0,55,43,71,90,91,100,100,94,75,87,50,76,25,69,0,1 -1849,7,56,36,89,45,100,42,60,38,20,0,0,48,5,100,5,1 -1850,100,64,98,100,51,78,52,39,48,0,0,4,31,38,95,57,8 -1851,12,67,34,92,100,100,77,71,77,51,89,22,40,0,0,17,3 -1852,13,87,5,54,40,24,6,0,3,38,0,77,44,95,100,100,5 -1853,100,100,66,89,48,75,34,60,17,45,10,30,3,15,0,0,1 -1854,78,94,3,100,22,66,100,35,62,0,0,25,44,66,41,96,8 -1855,2,70,0,100,51,100,65,67,41,33,6,4,47,0,100,2,2 -1856,0,75,33,89,77,100,79,72,61,37,55,0,55,20,100,27,7 -1857,19,92,0,51,17,7,64,0,100,29,91,77,50,100,4,87,0 -1858,60,100,32,69,0,37,42,29,100,29,85,51,68,38,60,0,4 -1859,0,61,34,91,51,100,48,60,45,19,4,0,48,1,100,3,1 -1860,76,91,100,100,89,75,84,51,85,23,61,2,23,0,0,22,3 -1861,82,100,37,86,66,70,87,86,100,52,92,18,49,1,0,0,9 -1862,33,75,46,100,78,86,57,56,100,40,96,11,46,0,0,13,3 -1863,0,82,33,100,77,93,50,66,71,47,100,24,58,7,11,0,3 -1864,36,100,65,90,66,59,48,28,25,0,0,28,47,36,100,37,7 -1865,0,74,29,100,65,86,73,44,64,0,31,8,60,35,100,40,7 -1866,100,100,56,90,20,66,0,34,9,1,55,0,67,31,26,46,6 -1867,100,100,78,83,56,57,40,31,44,3,92,8,46,14,0,0,6 -1868,34,85,62,100,79,76,51,49,90,36,100,13,48,0,0,7,3 -1869,0,93,49,100,100,100,80,66,61,27,51,0,44,35,95,37,7 -1870,43,100,30,64,49,28,30,0,0,25,18,59,58,73,100,78,5 -1871,73,90,0,100,2,65,57,30,20,0,2,34,59,69,100,98,8 -1872,42,100,19,71,0,40,50,25,100,35,99,60,68,31,49,0,4 -1873,81,100,42,80,0,49,57,38,100,49,68,70,67,35,63,0,4 -1874,92,69,36,74,0,87,52,100,98,86,100,56,79,28,58,0,9 -1875,0,44,37,59,71,78,100,100,88,82,70,55,55,28,43,0,1 -1876,50,100,27,82,0,58,38,42,100,55,86,48,55,24,34,0,4 -1877,100,88,100,100,76,73,52,44,40,16,80,0,56,20,0,1,6 -1878,100,98,66,100,38,98,32,73,59,60,48,37,24,19,0,0,5 -1879,44,68,62,40,100,2,0,0,25,38,98,71,96,100,6,73,8 -1880,0,70,22,99,50,100,45,59,28,22,39,0,69,4,100,8,2 -1881,100,100,52,82,15,54,0,21,44,0,96,17,66,43,7,30,6 -1882,14,96,77,100,30,99,20,60,66,61,100,32,54,0,0,17,5 -1883,81,71,97,100,36,89,74,72,95,74,100,34,59,6,0,0,9 -1884,100,100,100,90,50,75,25,60,0,45,0,30,0,15,0,0,1 -1885,20,88,29,53,73,35,42,0,0,3,20,59,53,98,100,100,5 -1886,0,98,38,100,61,62,61,16,39,0,18,37,58,41,100,37,7 -1887,0,71,56,80,100,100,93,86,74,64,59,43,48,20,59,0,1 -1888,100,100,68,92,35,67,7,35,0,0,43,2,50,26,9,11,6 -1889,99,69,77,100,51,72,88,79,100,59,84,15,38,0,0,7,9 -1890,52,100,49,99,0,63,99,53,100,81,81,84,77,42,86,0,4 -1891,31,100,12,74,0,46,55,42,100,51,78,59,78,29,83,0,4 -1892,47,82,17,73,0,34,25,0,71,9,100,44,97,85,55,100,0 -1893,81,98,26,93,59,67,95,100,88,66,100,24,48,0,0,14,9 -1894,16,71,44,100,64,71,40,31,0,11,23,12,66,0,100,15,2 -1895,100,89,68,100,22,89,0,55,49,66,83,43,63,9,14,0,5 -1896,100,98,56,100,20,77,0,48,3,17,44,0,53,22,11,40,6 -1897,0,97,40,100,68,74,58,34,51,0,24,34,48,56,100,60,7 -1898,34,100,17,85,0,38,27,0,82,9,100,53,60,87,5,89,0 -1899,76,49,70,92,27,100,0,66,3,19,41,0,80,21,100,57,0 -1900,25,84,64,100,82,71,42,52,95,42,100,9,46,0,0,12,3 -1901,37,100,10,79,0,41,56,31,97,53,100,76,74,38,49,0,4 -1902,100,100,70,100,27,97,5,62,34,58,61,36,41,1,0,0,5 -1903,83,31,100,71,67,100,24,87,0,46,8,0,51,0,82,36,0 -1904,0,67,35,78,71,95,100,100,83,75,67,51,50,26,42,0,1 -1905,90,100,46,77,20,49,12,20,51,0,100,17,64,41,0,44,6 -1906,0,80,35,100,75,96,59,62,100,56,99,22,64,0,24,6,3 -1907,0,86,29,100,62,95,51,62,31,32,34,2,69,0,100,6,2 -1908,6,78,53,100,61,70,55,54,100,35,82,3,21,0,0,26,3 -1909,100,91,0,91,41,60,92,25,20,0,0,29,55,64,88,100,8 -1910,0,100,40,68,79,35,72,0,49,23,74,59,100,94,43,86,8 -1911,80,70,57,100,15,73,0,30,34,0,83,15,100,56,75,88,0 -1912,12,91,0,50,65,45,67,0,13,9,4,65,31,100,100,99,5 -1913,38,78,73,100,92,70,58,48,100,39,85,6,33,0,0,28,3 -1914,12,91,79,100,45,95,29,63,88,68,100,24,52,0,0,23,5 -1915,45,99,9,100,0,75,36,56,100,63,98,49,80,25,82,0,4 -1916,66,100,31,74,6,47,0,20,41,0,100,16,84,36,16,23,6 -1917,7,80,0,80,29,100,99,99,100,90,78,59,61,28,46,0,7 -1918,46,90,70,87,16,80,0,94,54,100,100,82,94,39,70,0,9 -1919,0,62,19,71,69,91,100,100,71,75,50,50,29,25,12,0,1 -1920,9,59,39,87,61,100,46,59,22,22,0,3,50,2,100,0,1 -1921,29,88,71,73,100,33,43,0,0,25,39,64,94,100,35,94,8 -1922,19,80,45,100,49,64,29,27,0,0,25,8,63,14,100,21,2 -1923,32,100,8,74,0,46,51,37,100,48,96,55,86,27,81,0,4 -1924,18,98,72,100,26,89,34,61,82,45,100,19,53,4,0,0,5 -1925,26,83,26,14,66,3,100,47,89,100,45,86,6,47,0,0,0 -1926,0,50,45,74,84,100,73,68,63,34,34,7,32,3,100,0,1 -1927,33,82,53,99,100,100,73,72,81,48,89,20,48,2,0,0,3 -1928,100,100,66,80,35,60,11,39,0,17,29,0,74,16,35,20,6 -1929,33,64,58,100,94,90,67,51,28,19,0,15,49,16,100,0,2 -1930,23,82,57,100,60,70,34,42,0,22,20,12,59,8,100,0,2 -1931,0,95,52,100,71,86,63,42,62,0,27,33,48,50,100,53,7 -1932,74,100,82,86,37,63,0,39,76,41,100,47,61,24,21,0,4 -1933,94,92,100,100,77,83,62,65,43,47,26,29,15,12,0,0,1 -1934,28,75,49,100,57,63,34,26,0,3,26,11,66,0,100,5,2 -1935,100,100,65,80,38,56,15,29,8,0,48,8,36,27,0,13,6 -1936,2,83,35,100,62,82,62,68,100,51,84,21,44,0,0,7,3 -1937,0,88,42,100,68,83,52,59,29,36,9,12,48,2,100,0,2 -1938,36,84,66,100,91,75,79,46,100,27,70,4,22,0,0,25,3 -1939,18,75,0,74,35,97,100,100,88,79,72,49,52,18,40,0,7 -1940,23,84,28,57,71,22,0,0,6,33,69,66,100,100,2,90,8 -1941,82,95,0,83,62,54,100,20,37,0,49,35,89,70,88,100,8 -1942,68,98,70,100,36,79,0,59,60,49,100,47,73,23,51,0,4 -1943,10,84,64,100,98,70,71,34,36,0,0,30,30,55,100,49,7 -1944,84,88,100,100,70,73,46,47,32,19,51,0,41,25,0,8,6 -1945,0,100,100,91,0,76,0,60,0,45,0,30,0,15,100,0,1 -1946,71,100,37,74,0,45,17,29,95,29,100,50,91,33,89,0,4 -1947,25,100,0,63,2,17,43,0,86,10,100,52,74,90,32,97,0 -1948,100,46,93,86,46,100,10,72,0,28,29,0,76,18,88,55,0 -1949,0,4,29,27,55,56,79,85,100,100,84,67,69,34,62,0,1 -1950,41,96,6,63,0,11,48,0,92,31,100,82,51,100,18,62,0 -1951,48,68,57,100,92,91,76,54,45,21,0,0,45,22,100,4,2 -1952,0,84,31,100,81,98,73,74,87,51,100,29,67,10,17,0,3 -1953,0,98,76,100,100,68,88,34,67,0,16,1,13,16,95,11,7 -1954,0,81,39,100,88,90,61,59,85,47,100,14,53,0,8,5,3 -1955,94,95,42,100,35,71,86,84,100,66,100,25,55,2,0,0,9 -1956,77,87,98,82,78,100,52,67,97,65,100,23,49,0,0,16,9 -1957,0,53,33,71,76,93,100,100,79,74,53,49,28,25,12,0,1 -1958,0,86,41,100,77,86,58,58,98,42,100,16,58,1,8,0,3 -1959,41,94,82,100,97,79,81,54,100,30,86,6,42,0,0,1,3 -1960,0,80,28,100,55,82,37,48,11,20,24,3,62,1,100,0,2 -1961,100,100,50,80,19,59,0,36,0,13,53,0,78,19,3,19,6 -1962,92,47,92,90,49,100,11,74,0,27,35,0,76,18,100,57,0 -1963,0,96,9,97,50,100,47,64,22,32,19,0,60,5,100,19,2 -1964,62,100,12,72,0,43,100,45,89,69,53,88,54,43,52,0,4 -1965,0,73,43,100,44,95,38,62,37,29,16,0,22,2,100,3,1 -1966,7,68,23,100,0,51,15,1,80,0,100,48,64,91,1,89,0 -1967,34,77,79,59,100,23,28,0,0,32,38,68,93,100,31,87,8 -1968,16,100,6,51,58,34,24,0,3,29,0,81,46,99,100,99,5 -1969,42,100,13,73,0,42,52,35,100,48,92,56,92,25,97,0,4 -1970,41,91,53,62,95,30,52,0,14,16,0,53,17,87,100,100,5 -1971,56,100,28,83,0,61,38,45,100,55,69,45,46,23,31,0,4 -1972,33,100,14,97,0,80,9,61,39,48,74,36,92,18,100,0,4 -1973,21,93,28,62,53,24,13,0,5,41,0,84,47,96,100,100,5 -1974,0,74,51,63,100,96,34,100,15,69,88,83,82,45,65,0,9 -1975,56,100,34,83,0,63,23,48,77,39,100,42,68,22,48,0,4 -1976,0,93,2,43,21,0,67,8,100,45,100,92,54,100,21,68,0 -1977,26,100,0,84,22,56,100,57,52,92,48,85,44,36,40,0,4 -1978,19,99,90,100,57,94,12,73,52,60,100,35,66,0,0,5,5 -1979,60,95,84,73,85,100,36,87,99,76,100,39,64,9,0,0,9 -1980,63,100,18,89,0,44,14,0,62,12,96,49,100,95,54,93,0 -1981,100,100,78,98,34,69,6,36,15,1,77,0,68,32,0,28,6 -1982,100,98,85,99,38,100,26,71,66,56,79,36,40,18,0,0,5 -1983,100,100,45,87,12,59,0,26,27,0,87,8,87,37,27,31,6 -1984,53,100,27,65,97,33,82,0,27,20,70,58,100,97,0,91,8 -1985,37,93,2,89,0,33,42,0,92,11,100,65,60,100,9,91,0 -1986,100,100,56,82,22,55,0,25,26,0,78,9,61,36,6,38,6 -1987,0,92,48,100,68,67,64,33,48,0,9,24,39,49,100,58,7 -1988,100,100,45,91,7,63,0,30,33,1,89,0,63,28,7,31,6 -1989,29,85,8,43,33,0,86,9,100,57,74,100,17,92,0,46,0 -1990,100,79,59,100,17,73,0,35,22,0,76,5,59,34,19,16,6 -1991,0,89,59,100,100,74,82,35,51,0,10,18,35,42,100,48,7 -1992,100,81,37,100,0,71,49,36,71,0,18,22,51,59,90,93,8 -1993,82,100,64,89,31,67,0,45,62,37,100,46,78,23,71,0,4 -1994,48,94,40,69,79,37,53,0,3,19,0,64,34,95,100,100,5 -1995,100,80,46,100,25,68,88,75,86,75,96,32,57,0,0,19,9 -1996,37,100,20,83,0,59,37,42,100,47,93,48,70,24,50,0,4 -1997,100,100,57,84,22,59,3,28,24,0,72,11,54,36,0,28,6 -1998,35,100,7,65,0,21,37,0,79,18,100,58,83,97,37,100,0 -1999,100,100,55,94,27,70,6,44,0,14,29,0,43,25,2,25,6 -2000,88,100,25,87,61,58,92,89,89,63,100,23,47,0,0,23,9 -2001,51,76,0,73,11,95,60,100,97,83,100,55,91,27,83,0,9 -2002,100,96,62,100,33,83,26,45,57,42,75,19,40,0,0,1,5 -2003,88,100,38,82,63,53,100,82,98,67,95,31,67,0,0,8,9 -2004,17,92,55,100,69,71,38,50,0,38,23,25,61,13,100,0,2 -2005,0,83,55,100,100,79,94,38,69,0,40,7,32,48,91,57,7 -2006,9,83,66,72,95,100,0,91,44,74,100,73,82,37,78,0,9 -2007,0,82,30,100,74,97,76,72,63,47,44,24,50,0,100,0,2 -2008,25,92,13,64,52,30,8,0,0,34,46,68,100,100,10,89,8 -2009,0,80,31,100,70,90,53,62,27,37,7,10,48,0,100,1,2 -2010,31,95,24,52,49,23,14,0,0,40,19,78,56,99,100,100,5 -2011,0,32,33,41,62,64,85,94,100,100,85,66,70,34,57,0,1 -2012,100,89,55,100,17,72,0,31,26,0,67,22,40,48,13,21,6 -2013,50,91,100,100,90,97,62,69,100,43,99,11,51,0,0,1,5 -2014,7,77,43,100,59,77,36,41,3,10,0,0,54,0,100,20,2 -2015,87,71,85,100,46,81,89,71,100,74,91,30,51,4,0,0,9 -2016,7,51,31,90,93,100,99,58,59,21,0,0,35,17,100,5,2 -2017,43,100,16,86,0,41,24,0,75,11,100,53,85,97,34,97,0 -2018,100,91,33,100,7,73,76,39,67,0,0,25,48,68,91,94,8 -2019,100,88,47,100,23,66,80,66,91,78,93,36,62,0,0,3,9 -2020,36,88,6,47,9,0,63,7,100,44,100,88,48,100,0,73,0 -2021,21,87,24,55,60,29,24,0,0,17,10,68,52,90,100,100,5 -2022,38,85,73,100,90,77,52,56,100,49,95,18,50,0,0,11,3 -2023,0,64,12,98,47,100,30,63,5,29,15,3,56,0,100,1,2 -2024,14,73,41,100,87,95,64,65,100,45,100,15,49,0,0,16,3 -2025,14,71,40,100,49,70,27,36,0,5,25,0,66,2,100,19,2 -2026,20,86,42,100,38,64,11,35,0,22,35,0,75,1,100,31,2 -2027,54,92,11,60,0,17,50,0,95,29,100,72,58,100,15,72,0 -2028,2,96,63,100,60,81,54,62,73,43,100,26,62,10,0,0,3 -2029,24,91,7,61,54,36,37,0,6,23,0,76,39,100,100,95,5 -2030,0,33,35,52,67,75,97,100,100,90,83,61,68,31,56,0,1 -2031,35,57,0,47,1,0,48,5,86,36,100,83,58,100,12,86,0 -2032,9,100,0,62,52,48,45,0,2,13,3,68,37,99,100,100,5 -2033,40,100,23,89,0,59,26,34,95,45,100,60,68,31,56,0,4 -2034,94,91,32,81,100,44,91,0,8,18,45,60,98,100,0,100,8 -2035,45,82,60,100,56,69,31,43,0,23,28,3,65,0,100,16,2 -2036,13,52,35,92,40,100,43,57,45,13,0,0,51,1,100,0,1 -2037,22,86,62,100,82,74,59,60,100,42,93,11,40,0,0,17,3 -2038,100,100,55,86,93,81,97,76,97,51,93,26,55,7,0,0,9 -2039,61,100,1,74,0,47,100,48,95,73,64,76,54,36,52,0,4 -2040,0,46,3,4,43,0,78,24,100,57,92,96,51,100,17,78,0 -2041,6,100,32,98,66,71,62,34,41,0,0,27,39,41,100,46,7 -2042,0,54,26,89,36,100,32,61,40,22,4,0,40,3,100,4,1 -2043,0,91,53,100,93,87,60,47,36,4,26,0,37,35,100,40,7 -2044,92,90,51,100,34,76,87,79,100,67,92,29,53,4,0,0,9 -2045,52,89,1,100,0,72,60,76,68,73,100,39,88,6,27,0,9 -2046,79,100,47,89,0,70,24,56,87,45,100,43,60,22,28,0,4 -2047,100,97,23,100,14,67,65,34,43,0,0,26,39,62,93,96,8 -2048,100,100,52,87,16,61,0,29,22,0,73,10,65,40,10,46,6 -2049,100,100,51,96,10,82,7,63,57,61,71,35,44,9,0,0,5 -2050,65,87,100,100,73,87,37,74,64,58,67,21,36,0,0,12,5 -2051,100,100,59,80,31,55,16,26,31,0,82,5,47,18,0,4,6 -2052,1,90,42,100,76,89,59,61,100,44,100,18,55,2,0,0,3 -2053,90,88,43,100,15,69,67,66,86,66,100,27,59,3,0,0,9 -2054,0,87,41,100,79,83,60,43,50,2,49,0,40,32,100,36,7 -2055,35,100,8,68,0,31,24,0,73,10,100,43,98,80,55,94,0 -2056,2,79,32,100,52,70,39,36,12,6,0,7,50,7,100,0,2 -2057,23,81,61,100,92,91,58,62,81,39,100,17,51,4,0,0,3 -2058,61,100,34,75,0,44,55,37,100,49,76,70,66,35,70,0,4 -2059,26,82,76,100,81,76,35,51,55,49,100,28,64,0,0,2,3 -2060,23,88,0,47,20,7,66,0,100,29,91,76,52,100,12,78,0 -2061,29,100,24,75,0,48,27,33,89,37,87,51,89,22,100,0,4 -2062,100,100,81,89,67,74,48,59,33,45,22,30,7,15,0,0,1 -2063,100,100,54,86,18,59,0,29,23,1,85,0,66,18,23,15,6 -2064,7,77,54,48,76,10,24,0,49,36,100,69,80,100,0,84,8 -2065,100,74,81,100,33,92,4,62,0,26,31,0,61,21,24,42,6 -2066,100,86,67,100,24,80,0,50,0,16,41,0,63,24,18,35,6 -2067,20,78,63,100,92,84,46,59,60,50,100,30,62,3,0,0,3 -2068,100,95,49,100,14,71,0,34,21,0,64,16,39,45,29,13,6 -2069,26,89,7,73,0,30,45,0,97,21,100,65,64,100,9,88,0 -2070,100,94,55,100,21,84,0,55,45,62,71,40,51,11,8,0,5 -2071,96,91,37,100,56,59,93,91,94,71,100,26,56,0,0,20,9 -2072,100,100,62,87,34,64,13,37,0,9,29,0,40,25,1,22,6 -2073,11,88,57,100,100,88,61,66,67,50,91,24,55,2,0,0,3 -2074,2,74,5,95,70,100,100,89,66,67,31,45,0,22,2,0,7 -2075,90,82,46,100,38,56,97,74,92,84,100,35,62,0,0,19,9 -2076,0,88,37,100,79,94,92,73,90,52,100,31,93,10,54,0,3 -2077,24,91,22,61,38,25,1,0,0,39,0,78,46,95,100,100,5 -2078,63,96,3,100,26,61,54,21,9,0,0,28,50,56,100,84,8 -2079,38,85,60,100,58,72,33,45,0,24,15,10,58,0,100,10,2 -2080,30,100,10,85,0,66,0,48,27,34,65,28,96,18,100,0,4 -2081,100,100,60,98,23,69,0,32,24,0,77,8,70,39,17,37,6 -2082,53,96,98,100,100,85,67,68,71,45,78,22,45,6,0,0,3 -2083,100,83,62,100,22,73,0,44,3,13,70,0,95,22,8,21,6 -2084,45,96,0,68,48,62,52,12,6,0,20,54,45,100,100,99,5 -2085,42,100,12,74,0,45,65,51,100,76,85,55,62,27,37,0,4 -2086,0,96,54,100,99,80,86,42,67,6,34,0,36,25,100,36,7 -2087,25,64,54,100,53,90,49,45,32,3,0,0,51,1,100,2,1 -2088,0,78,57,74,91,100,10,94,23,74,100,80,86,40,85,0,9 -2089,37,90,73,100,100,90,64,72,83,45,83,16,43,3,0,0,3 -2090,100,100,63,82,35,63,10,43,0,21,14,0,63,2,35,11,6 -2091,44,100,2,69,0,38,71,39,100,50,60,74,56,37,58,0,4 -2092,0,97,46,100,68,71,62,34,43,0,22,14,49,29,100,31,7 -2093,73,100,38,88,11,74,0,55,23,40,62,31,95,18,100,0,4 -2094,65,100,0,89,67,60,100,24,38,0,29,38,55,78,3,99,8 -2095,16,89,1,48,23,9,72,0,100,34,87,76,45,100,0,83,0 -2096,42,100,44,88,11,57,0,33,77,40,100,64,91,34,93,0,4 -2097,1,80,50,69,100,100,1,90,0,62,97,81,97,41,86,0,9 -2098,24,96,19,69,58,30,16,0,2,42,0,90,52,100,100,94,5 -2099,37,95,7,60,65,60,65,11,0,0,16,50,30,95,100,100,5 -2100,78,100,17,99,24,73,83,80,100,68,98,30,61,2,0,0,9 -2101,24,96,91,100,100,95,32,92,25,62,87,51,62,0,0,6,5 -2102,100,100,55,85,15,56,8,21,56,0,99,24,53,45,0,26,6 -2103,36,92,7,77,0,30,35,0,81,15,100,58,76,99,27,100,0 -2104,46,85,75,100,79,72,53,56,100,46,95,15,49,0,0,15,3 -2105,0,87,56,98,100,100,84,80,67,60,56,40,51,20,44,0,1 -2106,46,100,6,79,0,48,67,43,100,73,77,61,45,31,19,0,4 -2107,72,100,28,85,0,61,100,67,89,84,64,84,64,41,59,0,4 -2108,50,67,75,100,67,81,56,40,43,2,0,0,57,2,100,4,1 -2109,74,100,10,93,15,69,77,80,78,66,100,30,66,4,0,0,9 -2110,29,76,50,100,70,74,50,42,18,16,0,0,50,7,100,7,2 -2111,15,84,0,64,18,97,78,98,100,100,79,65,68,29,65,0,7 -2112,19,75,64,99,100,100,48,90,5,63,53,51,47,2,0,0,5 -2113,46,81,15,59,51,32,31,0,0,8,14,56,50,90,100,100,5 -2114,100,100,50,90,20,61,0,29,18,0,76,6,62,31,5,33,6 -2115,0,84,53,100,94,76,94,38,77,0,38,3,41,28,100,36,7 -2116,15,95,69,100,75,66,51,33,22,0,0,9,30,29,100,33,7 -2117,51,88,58,54,87,20,66,0,25,30,0,63,31,94,100,100,5 -2118,16,82,51,50,100,17,13,0,0,32,60,65,89,100,16,84,8 -2119,0,100,38,98,48,59,38,19,24,0,18,39,58,45,100,46,7 -2120,8,92,37,100,66,67,59,24,27,0,0,32,50,28,100,24,7 -2121,19,60,33,88,100,100,93,70,51,48,98,27,63,0,0,0,3 -2122,0,96,42,100,62,62,56,19,39,0,11,36,51,52,100,57,7 -2123,56,100,31,68,0,38,27,25,83,22,100,50,91,34,76,0,4 -2124,92,75,51,67,51,98,94,100,100,63,84,28,49,0,0,2,9 -2125,38,100,2,73,0,42,69,34,100,52,83,62,62,31,45,0,4 -2126,0,91,43,100,89,95,100,59,84,23,66,0,38,32,84,33,7 -2127,88,82,39,100,4,68,100,35,84,0,0,22,49,62,84,93,8 -2128,100,92,66,100,39,82,42,57,70,38,64,9,32,0,0,8,5 -2129,0,78,50,86,100,100,85,80,73,60,58,40,50,19,31,0,1 -2130,11,91,56,100,100,92,73,73,51,52,55,28,47,6,0,0,3 -2131,58,85,100,100,65,96,24,70,23,55,70,48,49,13,0,0,5 -2132,13,95,71,100,93,71,54,42,94,42,100,12,43,0,0,20,3 -2133,37,76,46,100,99,95,74,61,100,50,91,18,42,0,0,15,3 -2134,52,100,20,70,0,29,34,0,82,18,100,60,69,94,16,86,0 -2135,100,88,69,100,34,82,8,56,0,23,26,0,50,22,22,42,6 -2136,31,75,53,100,68,77,49,45,21,16,0,1,50,5,100,0,2 -2137,37,100,0,77,13,53,74,60,100,82,68,55,40,28,18,0,4 -2138,42,100,4,70,0,34,60,32,100,57,98,71,70,35,47,0,4 -2139,19,86,17,47,59,32,21,0,1,38,0,90,49,98,100,100,5 -2140,41,100,0,75,11,49,96,50,100,56,52,70,54,35,57,0,4 -2141,81,100,27,92,0,59,2,22,47,0,95,23,100,61,88,97,0 -2142,91,98,43,100,45,75,95,100,100,76,95,43,61,14,0,0,9 -2143,0,67,27,77,67,93,100,100,92,75,85,50,79,25,75,0,1 -2144,38,85,75,100,92,84,71,58,100,37,82,11,41,0,0,6,3 -2145,55,92,100,100,73,86,43,59,72,40,89,12,46,0,0,0,5 -2146,60,100,20,91,32,52,45,12,0,0,5,35,53,54,100,73,8 -2147,44,100,8,78,0,41,14,6,66,0,99,30,100,67,56,78,0 -2148,0,90,45,100,62,76,28,53,60,42,100,26,62,6,13,0,3 -2149,29,100,12,82,0,62,1,41,31,32,64,29,93,20,100,0,4 -2150,100,100,57,98,18,90,0,62,22,55,57,40,42,12,4,0,5 -2151,0,90,41,100,69,86,59,64,45,43,33,22,49,3,100,0,2 -2152,0,40,38,60,74,80,100,100,76,76,55,50,38,25,22,0,1 -2153,8,62,17,100,61,88,41,44,0,11,8,22,58,0,100,14,2 -2154,15,85,9,47,70,44,54,0,9,12,0,68,37,97,100,100,5 -2155,100,100,57,85,21,58,0,29,13,0,66,8,57,33,6,26,6 -2156,0,91,15,56,44,22,15,0,36,34,82,67,100,100,5,95,8 -2157,0,69,39,100,82,84,70,40,41,0,6,13,44,41,100,40,7 -2158,0,79,31,100,79,95,51,66,82,50,100,21,58,0,12,12,3 -2159,10,76,0,34,26,0,69,9,100,42,97,84,57,100,14,87,0 -2160,13,58,40,100,49,97,46,45,25,3,0,1,50,1,100,0,1 -2161,89,68,81,100,34,80,34,40,30,0,0,8,38,42,100,59,8 -2162,0,70,33,100,83,97,78,59,44,26,2,0,42,1,100,2,2 -2163,0,95,56,100,61,80,51,40,44,0,17,27,41,39,100,47,7 -2164,0,62,34,93,81,100,85,61,53,28,11,17,56,19,100,0,2 -2165,0,100,75,97,100,71,61,36,32,0,83,4,81,24,0,23,7 -2166,28,100,1,75,0,45,70,38,100,66,88,58,60,29,34,0,4 -2167,0,90,32,100,66,77,45,39,26,0,1,20,45,34,100,39,7 -2168,35,100,0,73,11,42,76,37,100,67,82,61,54,30,27,0,4 -2169,76,100,53,91,0,71,33,56,100,47,97,48,47,26,17,0,4 -2170,100,100,65,92,27,69,0,40,8,7,58,0,87,21,41,33,6 -2171,91,100,77,80,11,58,0,37,97,45,100,50,51,26,29,0,4 -2172,0,74,18,100,50,94,45,62,32,32,24,4,62,0,100,1,2 -2173,0,98,53,100,84,72,62,34,43,0,1,26,44,37,100,38,7 -2174,26,100,6,58,59,94,100,50,61,0,0,0,30,60,16,98,9 -2175,62,97,85,100,42,77,49,67,100,57,94,23,50,0,0,9,5 -2176,33,99,0,55,1,4,66,0,100,45,91,96,34,100,13,51,0 -2177,51,82,84,100,99,79,80,55,100,29,79,4,36,0,0,19,3 -2178,0,92,35,95,100,100,93,79,59,41,36,0,12,16,76,22,7 -2179,0,69,27,95,66,100,76,66,61,31,37,1,58,0,100,6,2 -2180,26,70,19,25,57,0,95,21,100,68,69,100,24,88,0,48,0 -2181,75,100,25,84,55,49,63,14,0,0,3,21,58,46,100,77,8 -2182,35,93,78,100,89,85,69,65,100,42,88,16,46,4,0,0,3 -2183,100,53,95,92,46,100,11,69,0,26,34,0,76,25,88,62,0 -2184,100,71,72,100,34,78,84,71,98,71,91,29,48,2,0,0,9 -2185,100,100,69,76,33,52,0,27,58,33,87,53,56,28,38,0,4 -2186,0,100,41,98,56,69,50,34,43,0,28,26,57,44,100,48,7 -2187,54,100,0,92,10,62,64,69,73,71,100,36,79,7,23,0,9 -2188,94,100,8,82,79,46,99,4,0,0,15,41,100,72,47,96,8 -2189,14,95,85,100,45,96,9,68,44,57,100,33,59,0,0,16,5 -2190,100,100,64,90,24,52,37,9,96,0,92,42,29,50,0,17,6 -2191,47,84,94,100,100,93,41,80,25,61,82,41,53,0,0,10,5 -2192,100,91,38,100,32,62,99,70,96,83,97,40,69,1,0,0,9 -2193,24,87,31,38,61,0,100,25,96,72,57,100,10,87,0,42,0 -2194,48,100,25,78,0,48,25,30,72,29,94,55,90,32,100,0,4 -2195,31,100,0,74,52,36,48,0,0,18,52,56,100,94,19,94,8 -2196,0,65,43,81,84,100,100,92,90,69,80,46,73,22,53,0,1 -2197,0,100,33,93,67,92,100,94,78,71,57,49,38,25,27,0,7 -2198,0,75,4,98,64,100,100,92,72,70,46,48,27,24,25,0,7 -2199,0,96,51,100,58,78,38,56,13,35,0,14,42,1,100,0,2 -2200,31,100,0,81,21,58,100,62,74,88,54,80,58,40,66,0,4 -2201,86,93,100,100,64,84,39,67,22,51,8,34,0,17,0,0,1 -2202,41,100,2,77,0,46,74,42,100,65,88,62,80,31,79,0,4 -2203,0,86,47,100,100,91,68,72,58,51,74,31,76,10,11,0,3 -2204,60,100,27,82,0,58,49,46,86,55,86,63,87,31,100,0,4 -2205,100,91,53,100,48,69,93,88,93,65,87,24,49,0,0,14,9 -2206,16,100,12,92,0,69,38,52,100,57,96,46,82,24,75,0,4 -2207,51,100,45,99,11,65,0,25,43,0,100,20,74,48,15,31,6 -2208,26,77,0,33,28,0,69,15,100,52,85,97,44,100,4,84,0 -2209,57,100,23,74,2,45,0,16,47,0,100,13,72,34,16,23,6 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-3488,0,63,16,90,100,100,90,73,53,40,22,6,11,0,48,18,7 -3489,0,74,35,100,100,96,94,63,52,35,0,11,5,0,77,6,2 -3490,43,89,93,100,66,91,40,64,82,43,100,15,52,0,0,1,5 -3491,43,81,11,69,78,51,100,17,27,0,0,32,14,80,84,100,5 -3492,36,100,24,70,0,38,49,33,95,47,87,55,96,21,100,0,4 -3493,16,75,41,100,52,64,32,27,0,0,21,9,62,2,100,14,2 -3494,56,100,27,79,0,39,12,0,66,15,100,51,93,93,38,93,0 -3495,19,100,0,61,3,23,48,0,97,27,100,66,62,97,10,81,0 -3496,38,100,37,81,12,55,0,28,52,27,100,42,86,26,65,0,4 diff --git a/jupyter/make_datasets.ipynb b/jupyter/make_datasets.ipynb index d7d5c0b..d25a6f3 100644 --- a/jupyter/make_datasets.ipynb +++ b/jupyter/make_datasets.ipynb @@ -10,11 +10,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from sklearn.datasets.samples_generator import make_blobs\n", "from matplotlib import pyplot\n", @@ -40,21 +50,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from sklearn.datasets import make_moons\n", - "X, y = make_moons(n_samples=100, noise=0.2)\n", + "X, y = make_moons(n_samples=150, noise=0.2)\n", "df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y))\n", "fix, ax = pyplot.subplots()\n", "grouped = df.groupby('label')\n", "for key, group in grouped:\n", " group.plot(ax=ax, kind='scatter', x='x', y='y',\n", " label=key, color=colors[key])\n", - "pyplot.show()\n", - "\n", - "df" + "pyplot.show()" ] }, { @@ -87,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -151,21 +172,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# USE TO CREATE 2D DATASETS\n", "import pandas as pd\n", "\n", - "d = {'x': [i[0] for i in noisy_circles[0]],\n", - " 'y': [i[1] for i in noisy_circles[0]],\n", - " 'cluster' : noisy_circles[1]}\n", + "d = {'x': [i[0] for i in noisy_moons[0]],\n", + " 'y': [i[1] for i in noisy_moons[0]],\n", + " 'cluster' : noisy_moons[1]}\n", "\n", "df = pd.DataFrame(d)\n", "\n", "df = df.reindex(['x', 'y', 'cluster'], axis = 1)\n", - "#df.to_csv('blobs.csv', sep=',', encoding='utf-8')" + "df.to_csv('moons.csv', sep=',', encoding='utf-8')" ] }, { @@ -205,13 +226,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "df = pd.read_csv(\"data/mfeat-kar.csv\")\n", - "df.to_csv('data/mfeat-kar.csv', sep=',', encoding='utf-8')" + "df = pd.read_csv(\"data/pendigits_train.csv\")\n", + "df.to_csv('data/penditits_tra.csv', sep=',', encoding='utf-8')" ] }, { diff --git a/jupyter/testbed.ipynb b/jupyter/testbed.ipynb index f0a33ba..09a904b 100644 --- a/jupyter/testbed.ipynb +++ b/jupyter/testbed.ipynb @@ -51,10 +51,118 @@ "# noisy_circles_3d.csv\n", "# overlap.csv\n", "# pendigit.csv\n", + "# moons.csv\n", "\n", "df = pd.read_csv(\"data/noisy_circles_3d.csv\", index_col=0)" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df_blobs = pd.read_csv(\"data/blobs_3d.csv\", index_col=0)\n", + "df_overlap = pd.read_csv(\"data/overlap.csv\", index_col=0)\n", + "df_circles = pd.read_csv(\"data/noisy_circles_3d.csv\", index_col=0)\n", + "df_moons = pd.read_csv(\"data/moons.csv\", index_col=0)\n", + "\n", + "dfs=[df_blobs, df_overlap, df_circles, df_moons]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "fontdict = {'fontsize': 20,\n", + " 'fontweight' : 500,\n", + " 'verticalalignment': 'baseline',\n", + " 'horizontalalignment': 'center'}" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'Moons')" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "colors = {0:'red', 1:'blue', 2:'green'}\n", + "fig = plt.figure(figsize=(20,20))\n", + " \n", + "#blobs\n", + "ax1 = fig.add_subplot(221, projection='3d')\n", + "grouped = df_blobs.groupby('cluster')\n", + "\n", + "i = 0\n", + "for key, group in grouped:\n", + " ax1.scatter(group['x'], group['y'], group['z'], color=colors[i])\n", + " i = i + 1 \n", + "ax1.set_title('Blobs 3D', fontdict=fontdict)\n", + "ax1.set_xlabel('x')\n", + "ax1.set_ylabel('y')\n", + "ax1.set_zlabel('z')\n", + "\n", + "#overlap\n", + "ax2 = fig.add_subplot(222, projection='3d')\n", + "grouped = df_overlap.groupby('cluster')\n", + "\n", + "i = 0\n", + "for key, group in grouped:\n", + " ax2.scatter(group['x'], group['y'], group['z'], color=colors[i])\n", + " i = i + 1\n", + "ax2.set_title('Overlap', fontdict=fontdict)\n", + "ax2.set_xlabel('x')\n", + "ax2.set_ylabel('y')\n", + "ax2.set_zlabel('z')\n", + " \n", + "#circles\n", + "ax3 = fig.add_subplot(223, projection='3d')\n", + "grouped = df_circles.groupby('cluster')\n", + "\n", + "i = 0\n", + "for key, group in grouped:\n", + " ax3.scatter(group['x'], group['y'], group['z'], color=colors[i])\n", + " i = i + 1 \n", + "ax3.set_title('Circles 3D', fontdict=fontdict)\n", + "ax3.set_xlabel('x')\n", + "ax3.set_ylabel('y')\n", + "ax3.set_zlabel('z')\n", + " \n", + "#moons\n", + "ax4 = fig.add_subplot(224)\n", + "grouped = df_moons.groupby('cluster')\n", + "i = 0\n", + "for key, group in grouped:\n", + " group.plot(ax=ax4, kind='scatter', x='x', y='y', color=colors[i])\n", + " i = i + 1\n", + "ax4.set_title('Moons', fontdict=fontdict)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -66,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -212,9 +320,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -327,7 +435,7 @@ " weight=\"bold\", fontsize=16)\n", " \n", " ax.set_title(titles[index], fontsize=16)\n", - " plt.savefig('noisy_circles_3d_' + str(rotation_num) + '.png')\n", + " plt.savefig('experiments/exp3/mfeat_pix_' + str(rotation_num) + '.png')\n", " " ] }, @@ -342,55 +450,501 @@ "name": "stdout", "output_type": "stream", "text": [ + "Cluster |Full |Greedy |One Shot |Delta Medoids|Random Select\n", + "-------------------------------------------------------------------------------\n", + "0 |160 |32 |30 |16 |16 \n", + "1 |160 |48 |49 |16 |16 \n", + "2 |160 |44 |46 |15 |15 \n", + "3 |160 |63 |65 |14 |14 \n", + "4 |160 |39 |44 |15 |15 \n", + "5 |160 |70 |69 |12 |12 \n", + "6 |160 |51 |51 |13 |13 \n", + "7 |160 |19 |18 |11 |11 \n", + "8 |160 |70 |73 |13 |13 \n", + "9 |160 |44 |42 |15 |15 \n", + "Full Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 0.95 0.93 0.94 40\n", + " 1 0.93 0.93 0.93 40\n", + " 2 0.98 1.00 0.99 40\n", + " 3 1.00 0.88 0.93 40\n", + " 4 0.91 1.00 0.95 40\n", + " 5 0.90 0.93 0.91 40\n", + " 6 0.97 0.97 0.97 40\n", + " 7 0.97 0.97 0.97 40\n", + " 8 0.88 0.93 0.90 40\n", + " 9 0.97 0.93 0.95 40\n", + "\n", + " micro avg 0.94 0.94 0.94 400\n", + " macro avg 0.95 0.95 0.94 400\n", + "weighted avg 0.95 0.94 0.94 400\n", + "\n", + "Delta Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 0.97 0.95 0.96 40\n", + " 1 1.00 0.95 0.97 40\n", + " 2 0.97 0.97 0.97 40\n", + " 3 0.97 0.95 0.96 40\n", + " 4 1.00 0.97 0.99 40\n", + " 5 0.90 0.93 0.91 40\n", + " 6 0.95 0.97 0.96 40\n", + " 7 0.97 0.95 0.96 40\n", + " 8 0.88 0.95 0.92 40\n", + " 9 0.93 0.95 0.94 40\n", + "\n", + " micro avg 0.95 0.95 0.95 400\n", + " macro avg 0.96 0.95 0.96 400\n", + "weighted avg 0.96 0.95 0.96 400\n", + "\n", + "Random Selection\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/hlavaj20/miniconda3/lib/python2.7/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n", + " warnings.warn(msg, DataConversionWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 0.95 0.92 40\n", + " 1 0.90 0.93 0.91 40\n", + " 2 0.97 0.95 0.96 40\n", + " 3 0.90 0.95 0.93 40\n", + " 4 0.93 0.95 0.94 40\n", + " 5 0.92 0.90 0.91 40\n", + " 6 0.97 0.93 0.95 40\n", + " 7 0.90 0.90 0.90 40\n", + " 8 0.95 0.88 0.91 40\n", + " 9 0.93 0.93 0.93 40\n", + "\n", + " micro avg 0.93 0.93 0.93 400\n", + " macro avg 0.93 0.93 0.93 400\n", + "weighted avg 0.93 0.93 0.93 400\n", + "\n", + "Greedy Selection\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 40\n", + " 1 1.00 0.95 0.97 40\n", + " 2 0.97 0.95 0.96 40\n", + " 3 0.97 0.95 0.96 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 0.90 0.95 0.93 40\n", + " 6 0.97 0.97 0.97 40\n", + " 7 0.95 0.97 0.96 40\n", + " 8 0.97 0.97 0.97 40\n", + " 9 0.95 0.97 0.96 40\n", + "\n", + " micro avg 0.97 0.97 0.97 400\n", + " macro avg 0.97 0.97 0.97 400\n", + "weighted avg 0.97 0.97 0.97 400\n", + "\n", "Rates for each method:\n", - "greedy_select: 0.0\n", - "one_shot_medoids: 0.0\n", - "random_select: 0.0238907849829\n", - "full_medoids: 0.0\n", + "greedy_select: 0.0309278350515\n", + "one_shot_medoids: 0.0471204188482\n", + "random_select: 0.0810810810811\n", + "full_medoids: 0.0582010582011\n", "Method that got the best result is full_medoids\n", "Creating plots.\n", "\n", "\n", "Dataset Rotated!\n", "\n", + "Cluster |Full |Greedy |One Shot |Delta Medoids|Random Select\n", + "-------------------------------------------------------------------------------\n", + "0 |160 |35 |31 |14 |14 \n", + "1 |160 |47 |41 |15 |15 \n", + "2 |160 |47 |50 |16 |16 \n", + "3 |160 |57 |63 |14 |14 \n", + "4 |160 |40 |44 |16 |16 \n", + "5 |160 |70 |65 |12 |12 \n", + "6 |160 |48 |53 |15 |15 \n", + "7 |160 |21 |23 |12 |12 \n", + "8 |160 |68 |73 |13 |13 \n", + "9 |160 |45 |43 |16 |16 \n", + "Full Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 0.95 1.00 0.98 40\n", + " 1 0.87 0.97 0.92 40\n", + " 2 0.97 0.97 0.97 40\n", + " 3 1.00 0.88 0.93 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 0.95 0.95 0.95 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 1.00 0.95 0.97 40\n", + " 8 0.93 0.93 0.93 40\n", + " 9 0.95 0.95 0.95 40\n", + "\n", + " micro avg 0.96 0.96 0.96 400\n", + " macro avg 0.96 0.96 0.96 400\n", + "weighted avg 0.96 0.96 0.96 400\n", + "\n", + "Delta Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 0.98 1.00 0.99 40\n", + " 1 1.00 0.97 0.99 40\n", + " 2 1.00 0.97 0.99 40\n", + " 3 1.00 0.97 0.99 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 0.98 1.00 0.99 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 0.95 1.00 0.98 40\n", + " 8 0.97 0.97 0.97 40\n", + " 9 1.00 0.97 0.99 40\n", + "\n", + " micro avg 0.99 0.99 0.99 400\n", + " macro avg 0.99 0.99 0.99 400\n", + "weighted avg 0.99 0.99 0.99 400\n", + "\n", + "Random Selection\n", + " precision recall f1-score support\n", + "\n", + " 0 0.90 0.93 0.91 40\n", + " 1 0.95 0.95 0.95 40\n", + " 2 1.00 0.95 0.97 40\n", + " 3 0.97 0.88 0.92 40\n", + " 4 0.97 0.93 0.95 40\n", + " 5 0.80 0.90 0.85 40\n", + " 6 0.93 0.95 0.94 40\n", + " 7 0.97 0.93 0.95 40\n", + " 8 0.81 0.88 0.84 40\n", + " 9 0.88 0.88 0.88 40\n", + "\n", + " micro avg 0.92 0.92 0.92 400\n", + " macro avg 0.92 0.92 0.92 400\n", + "weighted avg 0.92 0.92 0.92 400\n", + "\n", + "Greedy Selection\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 40\n", + " 1 1.00 0.97 0.99 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 1.00 0.97 0.99 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 0.98 1.00 0.99 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 1.00 1.00 1.00 40\n", + " 8 0.98 1.00 0.99 40\n", + " 9 1.00 1.00 1.00 40\n", + "\n", + " micro avg 0.99 0.99 0.99 400\n", + " macro avg 1.00 0.99 0.99 400\n", + "weighted avg 1.00 0.99 0.99 400\n", + "\n", "Rates for each method:\n", - "greedy_select: 0.0\n", - "one_shot_medoids: 0.0\n", - "random_select: 0.0526315789474\n", - "full_medoids: 0.00671140939597\n", + "greedy_select: 0.00502512562814\n", + "one_shot_medoids: 0.0126582278481\n", + "random_select: 0.0928961748634\n", + "full_medoids: 0.0416666666667\n", "Method that got the best result is full_medoids\n", "Creating plots.\n", "\n", "\n", "Dataset Rotated!\n", "\n", + "Cluster |Full |Greedy |One Shot |Delta Medoids|Random Select\n", + "-------------------------------------------------------------------------------\n", + "0 |160 |55 |53 |15 |15 \n", + "1 |160 |67 |70 |14 |14 \n", + "2 |160 |62 |63 |12 |12 \n", + "3 |160 |84 |87 |7 |7 \n", + "4 |160 |64 |66 |12 |12 \n", + "5 |160 |96 |98 |9 |9 \n", + "6 |160 |70 |76 |14 |14 \n", + "7 |160 |31 |34 |13 |13 \n", + "8 |160 |89 |94 |5 |5 \n", + "9 |160 |64 |68 |12 |12 \n", + "Full Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 0.89 0.97 0.93 40\n", + " 1 0.90 0.95 0.93 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 0.97 0.95 0.96 40\n", + " 4 0.95 1.00 0.98 40\n", + " 5 0.93 0.93 0.93 40\n", + " 6 0.97 0.97 0.97 40\n", + " 7 0.97 0.95 0.96 40\n", + " 8 0.97 0.82 0.89 40\n", + " 9 0.95 0.95 0.95 40\n", + "\n", + " micro avg 0.95 0.95 0.95 400\n", + " macro avg 0.95 0.95 0.95 400\n", + "weighted avg 0.95 0.95 0.95 400\n", + "\n", + "Delta Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 40\n", + " 1 1.00 1.00 1.00 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 1.00 1.00 1.00 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 1.00 1.00 1.00 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 1.00 1.00 1.00 40\n", + " 8 1.00 1.00 1.00 40\n", + " 9 1.00 1.00 1.00 40\n", + "\n", + " micro avg 1.00 1.00 1.00 400\n", + " macro avg 1.00 1.00 1.00 400\n", + "weighted avg 1.00 1.00 1.00 400\n", + "\n", + "Random Selection\n", + " precision recall f1-score support\n", + "\n", + " 0 0.93 1.00 0.96 40\n", + " 1 0.93 0.93 0.93 40\n", + " 2 0.97 0.97 0.97 40\n", + " 3 0.97 0.72 0.83 40\n", + " 4 0.90 0.93 0.91 40\n", + " 5 0.81 0.85 0.83 40\n", + " 6 0.82 0.90 0.86 40\n", + " 7 0.90 0.95 0.93 40\n", + " 8 0.97 0.88 0.92 40\n", + " 9 0.86 0.90 0.88 40\n", + "\n", + " micro avg 0.90 0.90 0.90 400\n", + " macro avg 0.91 0.90 0.90 400\n", + "weighted avg 0.91 0.90 0.90 400\n", + "\n", + "Greedy Selection\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.98 1.00 0.99 40\n", + " 1 1.00 1.00 1.00 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 1.00 1.00 1.00 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 1.00 1.00 1.00 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 1.00 1.00 1.00 40\n", + " 8 1.00 0.97 0.99 40\n", + " 9 1.00 1.00 1.00 40\n", + "\n", + " micro avg 1.00 1.00 1.00 400\n", + " macro avg 1.00 1.00 1.00 400\n", + "weighted avg 1.00 1.00 1.00 400\n", + "\n", "Rates for each method:\n", - "greedy_select: 0.0169491525424\n", + "greedy_select: 0.00250626566416\n", "one_shot_medoids: 0.0\n", - "random_select: 0.048951048951\n", - "full_medoids: 0.00671140939597\n", + "random_select: 0.108033240997\n", + "full_medoids: 0.0526315789474\n", "Method that got the best result is full_medoids\n", "Creating plots.\n", "\n", "\n", "Dataset Rotated!\n", "\n", + "Cluster |Full |Greedy |One Shot |Delta Medoids|Random Select\n", + "-------------------------------------------------------------------------------\n", + "0 |160 |50 |51 |11 |11 \n", + "1 |160 |69 |67 |13 |13 \n", + "2 |160 |64 |62 |12 |12 \n", + "3 |160 |86 |90 |10 |10 \n", + "4 |160 |62 |63 |11 |11 \n", + "5 |160 |103 |101 |8 |8 \n", + "6 |160 |69 |75 |12 |12 \n", + "7 |160 |23 |27 |13 |13 \n", + "8 |160 |103 |101 |7 |7 \n", + "9 |160 |67 |71 |14 |14 \n", + "Full Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 0.95 0.95 0.95 40\n", + " 1 0.93 0.97 0.95 40\n", + " 2 1.00 0.97 0.99 40\n", + " 3 0.92 0.88 0.90 40\n", + " 4 0.97 0.97 0.97 40\n", + " 5 0.95 0.88 0.91 40\n", + " 6 0.93 0.95 0.94 40\n", + " 7 0.89 1.00 0.94 40\n", + " 8 0.90 0.88 0.89 40\n", + " 9 1.00 0.97 0.99 40\n", + "\n", + " micro avg 0.94 0.94 0.94 400\n", + " macro avg 0.94 0.94 0.94 400\n", + "weighted avg 0.94 0.94 0.94 400\n", + "\n", + "Delta Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 40\n", + " 1 1.00 1.00 1.00 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 1.00 1.00 1.00 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 1.00 1.00 1.00 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 1.00 1.00 1.00 40\n", + " 8 1.00 1.00 1.00 40\n", + " 9 1.00 1.00 1.00 40\n", + "\n", + " micro avg 1.00 1.00 1.00 400\n", + " macro avg 1.00 1.00 1.00 400\n", + "weighted avg 1.00 1.00 1.00 400\n", + "\n", + "Random Selection\n", + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.97 0.90 40\n", + " 1 0.92 0.85 0.88 40\n", + " 2 0.97 0.97 0.97 40\n", + " 3 0.92 0.82 0.87 40\n", + " 4 0.94 0.85 0.89 40\n", + " 5 0.68 0.68 0.68 40\n", + " 6 0.91 0.97 0.94 40\n", + " 7 0.95 0.97 0.96 40\n", + " 8 0.89 0.82 0.86 40\n", + " 9 0.86 0.93 0.89 40\n", + "\n", + " micro avg 0.89 0.89 0.89 400\n", + " macro avg 0.89 0.89 0.88 400\n", + "weighted avg 0.89 0.89 0.88 400\n", + "\n", + "Greedy Selection\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 40\n", + " 1 1.00 1.00 1.00 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 1.00 1.00 1.00 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 1.00 1.00 1.00 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 1.00 1.00 1.00 40\n", + " 8 1.00 1.00 1.00 40\n", + " 9 1.00 1.00 1.00 40\n", + "\n", + " micro avg 1.00 1.00 1.00 400\n", + " macro avg 1.00 1.00 1.00 400\n", + "weighted avg 1.00 1.00 1.00 400\n", + "\n", "Rates for each method:\n", "greedy_select: 0.0\n", "one_shot_medoids: 0.0\n", - "random_select: 0.0526315789474\n", - "full_medoids: 0.00671140939597\n", + "random_select: 0.129943502825\n", + "full_medoids: 0.0610079575597\n", "Method that got the best result is full_medoids\n", "Creating plots.\n", "\n", "\n", "Dataset Rotated!\n", "\n", + "Cluster |Full |Greedy |One Shot |Delta Medoids|Random Select\n", + "-------------------------------------------------------------------------------\n", + "0 |160 |38 |35 |15 |15 \n", + "1 |160 |53 |48 |13 |13 \n", + "2 |160 |43 |43 |13 |13 \n", + "3 |160 |64 |67 |16 |16 \n", + "4 |160 |45 |45 |15 |15 \n", + "5 |160 |77 |79 |11 |11 \n", + "6 |160 |52 |53 |15 |15 \n", + "7 |160 |21 |24 |15 |15 \n", + "8 |160 |76 |81 |13 |13 \n", + "9 |160 |47 |47 |13 |13 \n", + "Full Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 0.95 0.97 0.96 40\n", + " 1 0.93 0.95 0.94 40\n", + " 2 1.00 0.97 0.99 40\n", + " 3 0.97 0.82 0.89 40\n", + " 4 0.93 0.97 0.95 40\n", + " 5 0.85 0.88 0.86 40\n", + " 6 0.90 0.93 0.91 40\n", + " 7 0.85 1.00 0.92 40\n", + " 8 0.93 0.95 0.94 40\n", + " 9 1.00 0.82 0.90 40\n", + "\n", + " micro avg 0.93 0.93 0.93 400\n", + " macro avg 0.93 0.93 0.93 400\n", + "weighted avg 0.93 0.93 0.93 400\n", + "\n", + "Delta Medoids\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 40\n", + " 1 1.00 1.00 1.00 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 1.00 1.00 1.00 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 1.00 1.00 1.00 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 1.00 0.97 0.99 40\n", + " 8 1.00 1.00 1.00 40\n", + " 9 0.98 1.00 0.99 40\n", + "\n", + " micro avg 1.00 1.00 1.00 400\n", + " macro avg 1.00 1.00 1.00 400\n", + "weighted avg 1.00 1.00 1.00 400\n", + "\n", + "Random Selection\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 1.00 0.92 40\n", + " 1 0.97 0.90 0.94 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 0.97 0.88 0.92 40\n", + " 4 0.95 1.00 0.98 40\n", + " 5 0.92 0.82 0.87 40\n", + " 6 0.93 0.97 0.95 40\n", + " 7 0.89 0.97 0.93 40\n", + " 8 0.94 0.85 0.89 40\n", + " 9 0.88 0.88 0.88 40\n", + "\n", + " micro avg 0.93 0.93 0.93 400\n", + " macro avg 0.93 0.93 0.93 400\n", + "weighted avg 0.93 0.93 0.93 400\n", + "\n", + "Greedy Selection\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 40\n", + " 1 1.00 1.00 1.00 40\n", + " 2 1.00 1.00 1.00 40\n", + " 3 1.00 1.00 1.00 40\n", + " 4 1.00 1.00 1.00 40\n", + " 5 1.00 1.00 1.00 40\n", + " 6 1.00 1.00 1.00 40\n", + " 7 1.00 0.97 0.99 40\n", + " 8 1.00 1.00 1.00 40\n", + " 9 0.98 1.00 0.99 40\n", + "\n", + " micro avg 1.00 1.00 1.00 400\n", + " macro avg 1.00 1.00 1.00 400\n", + "weighted avg 1.00 1.00 1.00 400\n", + "\n", "Rates for each method:\n", - "greedy_select: 0.00334448160535\n", - "one_shot_medoids: 0.0204081632653\n", - "random_select: 0.0830324909747\n", - "full_medoids: 0.0169491525424\n", + "greedy_select: 0.00250626566416\n", + "one_shot_medoids: 0.00250626566416\n", + "random_select: 0.078167115903\n", + "full_medoids: 0.078167115903\n", "Method that got the best result is full_medoids\n", "Creating plots.\n", "\n", @@ -401,7 +955,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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kaK0VN09yvxXY7mOSfNfM/MFJjktybJIzknxqBfYJACNpI4F1SNBaK65N8qH0M2nHJLnPMl7zZ0m+tIv1d8v2BuQzSd6Q5FZJnpjkkCQ/Mm3jhr2qMQDsG9pIYB1yj9Za8aUk70hyXpKvDtrmvWaen5Xk6iQXJ/nEtOxmSe4yaF8AsFK0kcA6tKwrWlV1UpIXJ9mY5GWttResaK1YnqcmOSzJdel9yd+fHfuU337m+eUzz6+YK3PhSlUQYP+mfVzDtJHAKtvtFa2q2pjkL5P8UJJ7JPnxqrrHSleMZfiW9Kb98CR3T/Kk9K4QC2468/y6mefXLlEGgGXTPq5x2khglS3nitb3JbmotfaZJKmqM5I8KsknV7JiLOHq9O4TF6V3pTgsyYPSu0BsSHJSdn/2rVayggAHDO3jWqONBNaQ5dyjdfvseLH90ux4wT1JUlVPq6rNVbV5VOVYxNb0G4KvSPKNJF9O8qbpedKHpz18ej7bj/0mM88PnXk+qq87wIFH+7jWaCOBNWQ5QWuxczttpwWtvbS1tqm1tunGV4slLXWmrS3y/HMzy45c4vlsGQD2hPZxrdFGAmvIcoLWpUnuMDN/dJLPr0x1DmCVfpbt8PTf8Vhw8MzypHdKeVCS26T3Pb95+hC0h0zrr0jyten5x2a2c2J6X/M7JbnntOwrST498D0AHFi0j/uKNhJYh5Zzj9Y5SY6rqjunn9t5QpKfWNFaHYhukeSZiyx/yDQlySnp3RvuleSERcpen+StM/MXJvl4+u+EHJvkWTPrbkjvTuH3QQD2lvZxX9FGAuvQboNWa+36qvql9NtLNyZ5eWvtvBWvGYv7YPrNvndM/42PQ6b5LUk+kB2HqE2SN6Y3//dO/yHG69PvKDg7O95ZAMAe0T6uQdpIYA2p1nbqTn7jN1o1fqOwhqzEvxtYSzZt2pTNmzcbf20w7SP7O+0jB4KqOnc5990u5x4tAAAA9oCgBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMJigBQAAMNhBK7HR448/Ph/5yEdWYtOwJmzcuHG1qwAratu2batdhf3S8ccfn82bN692NWDFVNVqVwHWDFe0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABhO0AAAABjtotSvAXnjzm1Ovf33qnHOSL3wh+cY3kmOOSXv4w9Oe9azkiCNWu4awqHZ4S3tgS45Octt88xuo3lapc2rx11RLe0pLbjctuCbZ8Mc7niNqR07bvWOSmya5IclVSX2ikg8ntW3xbQPAmnB4khPS28ejsv1/6G9N8tGZcsckOXkX2zk9yUUz85XkvknuneSIJNcnuTTJ2UkuufHVZtcErXVow1/9VerMM3dceN55qfPOS3vd67Jt8+bkFrdYncrBrtws/Qt/T9wv20PWItq3TkHskJmFG5MclbSjWnLrpN4kaAGwht08vb0b7TFJvmtm/uAkxyU5NskZST61Avvkm3QdXI8OPTTbfv7nc8NHP5obrr46N3zgA2lHH50kqc9+NvXyl69yBWEJ16ZfYXpdJZt3X7zdoqX9YEu+votC35ntIetTSf1hpV5WyTemZd+dtIPbjao2AKyoa5N8KMlrk5yzzNf8WZJT5qbZq1l3y/aQ9Zkkf5zkFelt6sYkPzI9smJ2G7Sq6uVVdXlVfWJfVIjd2/b3f5/253+efO/3Jje5SXLf+6b98i9vL/AppydYm+q/KhvO3JD6ZCVf3X359oh+pares4srUttmtn9hpa6r1OcruXJauCEaElaMNhIY4ktJ3pHkvCyrfVyWe808PyvJ1UkuTrLwbXWzJHcZtC8WtZwrWqclOWmF68GeuNnNdl527bXbn09Xt2A9a/dsyV2TfC7JR3ZR8LwkX5tec7eWdmhLu13vMpgkuTSpa3UdZMWcFm0ksBqemuR5SX49yY8nucPc+tvPPL985vkVS5RhuN0Grdba2Umu2gd1YW9ddlnqr/4qSdIOPzztp35qlSsEN067SUt7WEtuSOqfKpWlg1J9uVJ/W8nWJMcl7dkt7amt34F6QVKvEbJYOdpIYNV8S3qPjcOT3D3Jk9K7Cy646czz62aeX7tEGYYbNhhGVT0tydOS5I53vOOozbI7l1ySDT/0Q6n//M+0DRuy7RWvSO4wf0oD1pf20Na//N+f1OW7Dkrtpi3t8TNXsGZ9a/ryq1egkrBM2kdgmKvTuxhelN7d8LAkD0rvJrgh/fr6hbvZhvOP+8ywwTBaay9trW1qrW068sgjR22WXTn//Gw44YTU+eenHXRQ2t/9XfLYx652reBGabdsvcG4JqkLKu22Le22M4NZVPqym/Zl7QdasvCVc05SL6jUn09XuG6btCe0tG8xGAarR/sIDLM1fdCMK9IHffpykjdl+wBQR6Rf4Up2vNfrJjPPD515Pup+MBZlePf1avPmbHjkI1Nbt6Ydfni2nXFG8vCHr3at4MZbGEHw8PRh2+cdlrSfbcl7k3pv7XAlq/61Ul+v5OtJ+8x0leuQ9H7r/77yVQeAFVVJFjt32BZ5/rkkC7/2c2SS/5h5npkyrBjDu69H7353NjzkIT1k3epW2XbmmUIW60JLSzusTzl4ZsXB2b58T31lZvvf09IOaf2q2LEzZa7d6VUAsHZU+pWow7NT+/jN5UnyqPSugrdJvz/r5unDtC+cpLwi3xwgKh+b2c6J6V3y75TkntOyryT59MD3wE52e0Wrql6d/vHcuqouTfJbrbW/XemKsbQNp56a+kr/32VdeWU2PuABO6xvJ5yQbe9+92pUDXbtFkl7xs5hqj24JQ/uzzf8zobU7+zcgXzb86dx3K9JNvzx9nNE9dHqIxQenOQ+SbvP3PYvSx/OFlaANhIY4hZJnrnI8odMU9J/J+sm6d3rT1ik7PVJ3jozf2GSj6f/ltaxSZ41s+6G9C6HN9yYSrM7uw1arbUf3xcVAdgb9Z+VvDxpD2i9i+BN039b60vpP2D8/kptc+cvK0MbCexTH0wfEOOO6b+Ddcg0vyXJB7LjMO5J8sb07oH3TnKr9DB2SZKzp0dWVLU2/ibxTZs2tY98ZFc/fAPr2yGHHLL7QrCObdu2La01CXWwTZs2tc2bN692NWDFVPna4IBwbmtt0+4KuUcLAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgMEELAABgsGqtjd9o1RVJLh6+YZZy6yRbV7sSsML8ne9bd2qtHbnaldjfaB9Xhe8O9nf+xve9ZbWRKxK02LeqanNrbdNq1wNWkr9zYG/47mB/52987dJ1EAAAYDBBCwAAYDBBa//w0tWuAOwD/s6BveG7g/2dv/E1yj1aAAAAg7miBQAAMJigBQAAMJigtQ5V1UlVdUFVXVRVv7Ha9YGRqurlVXV5VX1itesCrC/aR/Z32sj1RdBaZ6pqY5K/TPJDSe6R5Mer6h6rWysY6rQkJ612JYD1RfvIAeK0aCPXDUFr/fm+JBe11j7TWvt6kjOSPGqV6wTDtNbOTnLVatcDWHe0j+z3tJHri6C1/tw+ySUz85dOywDgQKZ9BNYUQWv9qUWWGaMfgAOd9hFYUwSt9efSJHeYmT86yedXqS4AsFZoH4E1RdBaf85JclxV3bmqDknyhCRvWuU6AcBq0z4Ca4qgtc601q5P8ktJ3pHk35O8prV23urWCsapqlcn+VCSu1fVpVX1lNWuE7D2aR85EGgj15dqTfdlAACAkVzRAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQAgAAGEzQYlFVdXJVtZnp61X16ar6/aq6ySrV6ZSqaqux78VU1Y9W1dlVdXlVfa2qLq6qf6iqk/ZiWyv23qrqxGn7G+aWHzN9tievxH4BDjRVdf+qOqOqLp3azS9X1TlV9btV9W2rXb9ZVbWlqk4btK1Dq+pXqupfq+or0/s+v6o3nN4RAAAgAElEQVT+rqqO24vtnVVVZ42o2yLbPrmqnrzE8lZVx6zEfjkwCVrszo8luX+SRyR5R5LnJPmjVa3RGlBVT0/yxiSfSvKU9ONz6rT6QatVryWcmOS3svO/98vSP9u37OsKAexvqupXk3wgyZFJnpvkwUmekN52Pi3Jy1evdivu1Ul+N8nrkzw2yeOTvDTJ3ZLcYxXrtZiTk+wUtNLbwvunt40wxEGrXQHWvI+11i6anr9zOjP1lKp6Rmtt22pWbJX9WpJ/aK09ZWbZu5P87/krR2tVa+26JB9e7XoArHdV9f+ln4R8cWvtV+ZWv7Wq/iD9xOWutnFwkutba2um58ZyVNWxSR6d5JmttRfPrHpbkj9dR23iFUmuWO16sH9ZF3/8rCn/nOSwJLdeWFBVR1bV31TVhVV1TVVdUlX/f1XdfvaFC93jquq4qnpLVV09dbd7/iLd2u5dVe+rqmur6nNV9bwkNV+Zqrp5Vf1FVX2+qq6rqgum7gs1U+bEab8/OtXzqqr6YlW9qKo2VtV9qur9VfXVqjqvqh62jONwRJIvLLZiPoBW1Z2r6lVVdcVUx49V1aN3t4OqOqiqnjN1v7hueo9/Mt91s6puWlUvmLp2XldVX6iq11fVbavqlPSrWUnyjYWuoNPrFu06WFU/OXX/uLaqtlbVK+e7vExdTk6vqidU1b9Px25zVT1gd+8LYD/07CRbp8edtNa+2lo7bWF+5vv3F6rqhVX1+STXJfnWaf2y2o2q+p6qetPUpn2tqj5QVQ9cpNwzpu/ta6fv6gfOrT9+qs+jFnntadW7Qm5c4r0fMT0ut038wap6V/Uuhl+tqndU1T2X2Pbs625dVS+Z/k9w3dQ2Pm2Rcnee2q0vTOU+U1UvntadleQHk/xAbb814qxp3U5dB6vq4Ko6dTp2X58eT60eihfKLHyWP1tVv1NVl1XVl6rqn6rq6N29L/Zvrmixp45J8l9JrpxZdkSSa9O7FV6R5HZJfjXJB6rq21tr185t441JXpHkRUl+OMlvJ7lkWpaqunX61aEvJPmZ9MbnWUnuOLuR6uHsLUm+N8nzk3w8vQvfn6Z33fjNuf3+WZI3pHdpOCG9a8dB6d07/ijJ56Zlb6iqO7XWtu7iOHw0yc9U1WeS/GNr7cLFClXVHZJ8JMnlSX5lOj6PT/L6qvrR1tqbdrGP09OPzx8m+WCS70jvmnFMeteMVNUhSd6Z5F5J/iD9CtUtkjwsyS2TvCzJ0endGx+Q5IZd7C9To/U3Sf5P+ud5uyS/n+S+VfW9rbWrZ4o/MMndkzwv/fP/3SRvrqpjWmtf2tV+APYXVXVQ+n/e39Ba+/oevvx/JTknvWvhxiTXLrfdqKrvTfK+JP+S5H8kuSbJzyX5v1X1/a21c6dyT0lv/05L/26/a3pXv5stVKK1dm5VnZPkZ5P848x7+9Ykj0vywtbaUu3H+Um+nOQFUwB5Z2vtPxcrWFWPmLb/liQ/OS1+dpL3VdV3t9YuWeJ1N0/vlnlYklOSfDa9nXtJVR3aWvvzqdyd09vna9JPMn4qyR2SPHTa1C+kt60bp/eaqe5L+bvp/f9+kvendy18bpJjk/zEXNnnpLfVT05ymyR/kuRV6X8bHKhaaybTTlN6H+aW/h/pg9L/0/7kJNcn+aXdvHZj+hdbS/LomeWnTMueNFf+40nOnJn/vSRfT3LHmWU3TT9b2GaWPXLa3slz23tZeji79TR/4lTu5XPl/nla/oCZZd89LfuZ3bzHuyX5t6lsm+r26iQPnSv3t+mN5K3mlr8zvVvmDsdmZv6B03Z/eu51T5yW32uaf/I0/yO7qOvCcT9obvkxs8dv+tz+M8l75so9YCr39JllW5J8McktZ5Ztmsr9xGr//ZpMJtO+mpLcdvru+4NF1h00O80sX/j+/eckNfea5bYb70ry70kOmVm2cVr2D9P8hvQTmW+f29bjp/2fNrPs5PSTcXeaWfb09Hb/6N0cgx+e6rzQJn46yV8k+fa5chcledfcsptPbeifzSw7K8lZM/MLJ/SOm3vt/55ee9A0//dJrk5yu13U9awk719k+clT3Y+Z5u85zZ8yV+650/Lvnvss3ztX7tem5UvWxbT/T7oOsjvnJ/lGkqvSv/z/prX2F/OFqurnp+5mV6d/Kf/HtOrui2xzfvCFT2THq1X3T/Lh1trCNtJa+2qSf5p73QlJtqUHnFmnJzlk2s6st83Nn5/kq621988tS3pQXFLrV7DunX6m6veSfCy9j/o7quq5M0VPSvLWJP9VvSvgQdPZz3ck+Z7pLN1iTkoPm6+fe92Z0/oTpseHJvlC2/WVseW6e/pZuFfNLpyOz8XZ+azch1prX5yZ//j0eMcAHDh26taeJFV1VHr7+c1p+h6f9Q+ttfl7snbbblTVYenfya9Nsm2mTCX5v9neRhw9Ta+Z28fr09vqWWck+VL61bEFP5vkLa21S5d++0lr7Z/SA8djkvz5tJ1fSPIvVfXg6Xgcl+QuSV41976uSfKhmTov5qT0q3yfXeSY3CrbB9x4aJI3t9Y+v6v6LtNCfU6fW74wP98mzv/fRpuIoMVuPTrJfZI8PP3L+xeq6qdnC1TVLyf5q2n9Y5J8X5L7TasXGwr+qrn56+bKfVv6lZV588uOSHJV64M6zPrCzPpZX5yb/3p6Y/BNbXu3j90OYd9au6G1dnZr7bmttQendyX4eJLfqqpbTsVuk+SnM9fYZvvIjbdaYvO3SQ+LV8+97vK5190qvcvjCAvHa7ERl76QnY/nDp/jzOewKsP/A6ySrelXW+b/Q701vf28T/qVl8Us9n27nHbjiPSrV89bpNwvJbnl1L1+4f7aHdrP1tr12fEWgLTezf8V6QNeHTTdx3WPJH+9RN130Pp9aG9srT29tXZ8ku9Pv0L2gpn3lfSTtvN1fmSWbg8XXnvCIq977bR+tk3cZSjcA0u1iUv9H2Ox/9sk2sQDmnu02J1PtGnUwap6d3p3uT+qqtdPV5mSPnztu1prv7rwoqmf9N66LL0rxrz5ZVclOaKqDmk79os/anq8MvtQa+3zVfWyJC9Oclx6P/Er0/vQ/+ESL1vqrNuV6Q33Tjc1z71ua3r3hhEWGomjFll3VJLNg/YDsN9orV1fVWcnechsezSFmc1JUlWPXOrliyxbTrtxUHqPjr9M7y63WL22VdVCSNih/ZyuBi0WbF6S5H8meVT6idYt6VeN9lhr7cNVdWb61ahke5v8nPQTs/N2dX/bleknGp+xxPoLpsetSW6/RJk9Ndsmfnpm+ar8H4P1SdBi2Vpr11XVs9JvZP2FbD+7dnh2vpn0STdiVx9K8qyqukObboytqpum9wGf9d70QTJ+LDt2d3ti+hf2ig1dPlu3Od8+PS6c8Xp7ehfG81prX9uDXbw9/QbhW7TW3rWLcmcmeUJV/fDUdWMxC2fVDkvylV1s64L0s55PSD/jmCSpqu9Pcqf0G3sB2NkL0++h+sP0ASxujOW0G9dV1fuSfE+Sf25L/9zKpen3aD0uO/6O12OzyP8BW2ufnsLRs9IHWfqdXWw7SVJVN0u/z+zLc8s3pp90XAh7F6QHt+9srb0ge+btSX45yX+01i7fRbkzkzymqr6ttbbU72Fdl5mBQHbhvdPjE9JvEVjwxOnx7GVsgwOcoMUeaa29aRqZ6Neq6i+mRuDtSZ5dVb+ZfhXnQUn++43YzYvSg9yZ1YcnXxh1cL7BeVv6KEB/XVVHJjkvvYvjU9NvSt7VqIE31ieq6j3pIyh+Nv1m3oenj/j0mpn7y56ffkzOrqq/SG9kbpl+FerY1tpiP5qY1tpZVfXqJK+rqj+dtrEtvQ/8w5M8e7pP7PT0/vSvrv47LR9Jb0Aeln5j8flJPjlt9ler6m1Jbmit7XR1qrV2Q1U9P8nfVNXp07Zvn97AfCrTqJAA7Ki19q6q+o30kfe+O/0q02fTu43dLf0/61/N4lew5i233fif6f/Zf0dV/W16oLl1+ki8G1trvzFd1frtJC+rqlek34d11/SrSkuNtvdX6SdUv5Hl/cjy3ZO8p6pemT5Ax+XpXRafOtX5F5I+2lNV/WKSf5xGzH1N+hWo26Z3M/yP1tqfLrGPF6UP4PG+qnpRemi7afrJzQe21haGpf+t9NGHP1hVv58++Mbtk5zUWlsY5fCT6bdBPD79StVXWmsXZE5r7bypHT5lugL4wfQA/Lwkr26t/dsyjg0HutUejcO0NqdsH33nrouse+i07lem+cPSuxtckX7F5M1J7py50Xqy9Oh3pyXZMrdsYdjaa9PvQXpe+jDwba7czdNHNros/SrWhelnE2umzInTfh+8yH4vXeT9tSSn7ub4/FySN6UPEnFtegP6L0l+PTMjQE1lj04fCfFzUx0vSz/z+ZPzx2budRvSu0n867SP/5qevzD9StdCuW9Jv7p48cz2X5fkNtP6jendSy5PD2ttWn5MFh+18Sen/VyX3jXilUm+ba7MliSnL3HsTtnVsTOZTKb9dUryA+kBYuH7/svpw7f/9uz36Mz371OX2M5u242p3Hekh6fLp+/sS6e26eFz5Z4x015tTh9NdktmRh2cKbtxatNeu8z3/K3p4fDsqZ7fSL8n+j1J/vsi5e+f/v+EL0712TK9h/vPlDkrM6MOTstumR64Pjsdk8vT/5/wzLlyd0kfJGvrdEw+k+RFM+uPSh9s5CvTZ3DWtPzkzIw6OC07OMmp07H7xvR4apKDd/dZZvv/PU5c7b9L0+pNNf0xAABwgKuqh6R3wXtw23XXdWA3BC0AgANcVd0lffTcFyW5rvWRA4EbwfDuAAA8L/3e5+vSh5cHbiRXtAAAAAZzRQsAAGCwFRnevapcJmO/dvzxuq6zf9uyZUu2bt1aq12P/Y32kf2d9pEDwbnnnru1tXbk7sqtSNdBDQn7O11u2d9t2rQpmzdvFrQG0z6yv9M+ciCoqnNba5t2V07XQQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEELQAAgMEOWu0KMDk8yQlJjk5yVLZ/Mm9N8tGZcsckOXkX2zk9yUUz85XkvknuneSIJNcnuTTJ2UkuufHVBoAVp40E1iFBa624eZL7rcB2H5Pku2bmD05yXJJjk5yR5FMrsE8AGEkbCaxDgtZacW2SD6WfSTsmyX2W8Zo/S/KlXay/W7Y3IJ9J8oYkt0ryxCSHJPmRaRs37FWNAWDf0EYC65B7tNaKLyV5R5Lzknx10DbvNfP8rCRXJ7k4ySemZTdLcpdB+wKAlaKNBNahZV3RqqqTkrw4ycYkL2utvWBFa8XyPDXJYUmuS+9L/v7s2Kf89jPPL595fsVcmQtXqoIA+zft4xqmjQRW2W6vaFXVxiR/meSHktwjyY9X1T1WumIsw7ekN+2HJ7l7kield4VYcNOZ59fNPL92iTIALJv2cY3TRgKrbDlXtL4vyUWttc8kSVWdkeRRST65khVjCVend5+4KL0rxWFJHpTeBWJDkpOy+7NvtZIVBDhgaB/XGm0ksIYs5x6t22fHi+2XZscL7kmSqnpaVW2uqs2jKscitqbfEHxFkm8k+XKSN03Pkz487eHT89l+7DeZeX7ozPNRfd0BDjzax7VGGwmsIcsJWoud22k7LWjtpa21Ta21TTe+WixpqTNtbZHnn5tZduQSz2fLALAntI9rjTYSWEOWE7QuTXKHmfmjk3x+ZapzAKv0s2yHp/+Ox4KDZ5YnvVPKg5LcJr3v+c3Th6A9ZFp/RZKvTc8/NrOdE9P7mt8pyT2nZV9J8umB7wHgwKJ93Fe0kcA6tJx7tM5JclxV3Tn93M4TkvzEitbqQHSLJM9cZPlDpilJTknv3nCvJCcsUvb6JG+dmb8wycfTfyfk2CTPmll3Q3p3Cr8PArC3tI/7ijYSWId2G7Raa9dX1S+l3166McnLW2vnrXjNWNwH02/2vWP6b3wcMs1vSfKB7DhEbZK8Mb35v3f6DzFen35HwdnZ8c4CAPaI9nEN0kYCa0i1tlN38hu/0arxG4U1ZCX+3cBasmnTpmzevNn4a4NpH9nfaR85EFTVucu573Y592gBAACwBwQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAACAwQQtAAD+X3v3G2NZXd9x/HPuvbM7M8sI7LJAkH8iDQlWQEASS4k1borG+CcmbWyqbaIWo+mDPtDIAwLxAcbE1BgS02isQU20sfrEJ7U0qSkGbBFbCwvWlkqKqLvsLsuys/Pnzr3n9MHI7iILu8B39s4sr1eyycy5d85+Z3P3/u77njNngGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACg2WIudXnHFFbnrrrvWYtewLpx77rmTHgHW1L59+yY9winp6quvzj333DPpMWDNzMzMTHoEWDcc0QIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACgmtAAAAIoJLQAAgGJCCwAAoJjQAgAAKCa0AAAAigktAACAYkILAACgmNACAAAoJrQAAACKCS0AAIBiQgsAAKCY0AIAACg2mPQAvLBm796c9vnPZ+rHP87goYfSDIdJkqdvvz2LH/rQsb9oPM7Wt789Uw88kCRpt27NnocfPlkjw/Nqp9vMXzOflXNWMjprlPRXt8/9YC6zO2cP3280N8r8m+Yz2jZKO9um63dphk2m9kxl9sHZbH5s83P2PZ4d59DVhzK8aJjxlnGaUZP+0/1M//d0tjyw5WR9i8AE9b/+9UzddNPz3r78k5+ku+yykzgRnJhupsvo+lHa89p0Z3eHX6EP7hpk8O/HfrneNV2Gfz5Md263umEhmb5j+vDt7elthh8dPu/fOfjeIIOfSIG15F93nevv2pXZL3/5RX3N7Be/eDiyYD0Zbxln8YrF496vnWuz/NrlZ23rZroMLxxmeOEwp3/v9Ew/emQxWdm2kv3v3J9upjty/36X0fZRlkfLQguAda2b6zK+dvyivmb8xvGRyGJdElrrXPuqV+XQRz6SlTe8IZvuvTezX/vaC96/99hjOe2zn007O5vewsJJmhJOTG/Yy+x/zmZq91SGrx5m8XXHjq7eQi9zP5jLpl9sSn++n3a2zdM3PJ3hRavvzC1dtnQ4tLqmy4E/PLAaWeNk7p65bP755jSjJuMzxxmdOTpp3x+wfiwtHv9NHVg3lpP+ff30ftVLe2Gb8dUvHF3t6W1Gvz9Khkk2HX/3U9+YSv+xfs2snDA/o7XOtRdemPlPfSrL73lP2u3bj3v/V33yk2kWF3Po5ptPwnTw4vQP9jN371ym/3c6vYXnf/oZPDXI7M7ZDA4M0oyb9A/2M/vQkVML0x75cPk1yxmfsbogbfmPLZl9aDb9xX56K71MPTGVmZ/NrNW3AwAlegd6mfrnqfT/q5+cwPvkoxtHyaZkcLdjJuvZcUOraZqvNE3zRNM0O0/GQLx009/5TjZ///tZueqqLHz4w5MeB0p06TKeG2fhdb9ZecZ5VnQNzz9y/nk73WbfH+/L7r/YnT0f2JOD1x9MO2h/e5dQxhq5fm2++OJsnpvL5osvztQHPpDmoYcmPRKUGF8+TntJm+ZXTfr3n9hRqpV3r2TpE0tZ+qulDN83zPi1L+40RV6aEzmidWeSt63xHLxMzf79mbv11nSDQZ7+3OeSnoOVbHxPvuvJPPHRJ7L3/XszvGiYZrnJGd87I5t+eeQ8ifFpRxaLxd9dzGjbKBkk7WltFq5YyFPvfCpd4xx21sydsUauS83u3WlGozS7d6f/7W9n0w03pLn//kmPBS9LN91l5a0ryTiZ+oepNGlO7Au3ZPUCVNNJe3GblT9ayehqp9avteO+Gu+67u4kT56EWXgZ5m67Lb19+7LwsY9ldPnlkx4H1kS3uctTf/hUll991IUyjnoW6x3sZds3tuWsr56VwZ7V0ylWzl3J8sXLgbVgjVxf2ksuycodd2R5584s7d+f5QcfzPjGG5MkzeJiBrfeOuEJ4eVZeetKsiXp/1s/vT0v/DK+GTYZ3D3Ipq9syua/3pzNd2xO/4dHjoCN3jxK1/dG5FoqO7GzaZqbktyUJOeff37VbjkB/Ucfzcy3vpV269Ys33hjBjt/6wyW8TiDnTvTnn122rPPnsyQ8BJs/e7WdE2XdkubQ1cdyuLrF5OpZP735rP571cv8d5bOrLQTP98OoMDq09rMz+bycHtB5Mko+2j5NGTPz8kz14fL7jggglPc2rrrr8+4+uvP/L5pZdm5QtfSP/SS5Mkvfvum9Ro8LK1Z7RpX98mC0n/kX7as3/r1Phe0p7dpjnUrP5ZbDK496iX+ivJ1L9Mpb2sTbe1SzYn3fYuza4TPCrGi1Z2flnXdV/quu7aruuu3bp1a9VuOQHNoUNJkt6TT2brO96RbTt2ZNuOHYdv7x04kG07dmTmq1+d1IjwkjVdk/58P6fdd9rhbaMzjpzu8MyRqxfcx8giwuQcvT5uP4GLGvEytMf4mcymOfbHsNE8c9b8bDL8s2GGH1z9c9h0MvzgMKM3rK6RXU7gaJUDWmvKpUrWu7ZNs39/ktXTHp7RLCyk2bdvUlPBS9KlSze9+qzeDY76nVeDLu306guk3lIv89fOp3+gn027NqV3qJd2ps3CVUcuw9R/+sipD9OPTGf+uvlkkCxdspSZnTNpRk0WLzvy/2XT4ydw7Vtgw5t673vT3nBD2ne9K91FF6V57LEMPv7xw7e3Rx3tgvWkS5c8c5Hco1+dT+VZvyPyxRi9eZRMJf2d/TR7mmRTMrputHo0K0mWkmavNx/WktBa53qPP57t1133nO1zt9+eudtvT5Ls3rUru3ftes59zjn33CRJu3Vr9jz88NoOCiegnWuz9/17n7N9/k3zmX/TfJLknL85J8Pzhll548qxdzJO5v517vCn/UP9zP1wLgdvOJh2rs2+P332GxDTP53O1BNTdd8EsG41v/51pm65Jbnllufc1p15Zkaf/vQEpoLj607vMvzo8DnbR28ZZfSW1SNU05+ZzvRnpp9zn6Wbl1Y/WEim7zjq9kEyvnZ87F+E3CVT/zSVZiy01tJxQ6tpmm8m+YMkZzVN83iS27qu+9u1Hgx45Zr+n+k0XZPRGaO0M23SJf2FfqZ+PZXZB2YztffZ4TS7cza9Q70sXLmQlbNWkiYZ7B9k5uGZzDzs92ixdqyR68vottvS/8530vzoR2l27UqWl9Odd17aHTsy+sQnkgsvnPSIcNL0H1w9+9AHTCEAAAQgSURBVKO9sE03t/ozWVlMer/sZXDfIL1fukL1Wmu6rv7kzCuvvLK76667yvcL68WVV1456RFgTe3bty8rKyve6ix2zTXXdPfcc8+kx4A1c+aZZ056BFhzS0tLP+667trj3U/KAgAAFBNaAAAAxYQWAABAMaEFAABQTGgBAAAUE1oAAADFhBYAAEAxoQUAAFBMaAEAABQTWgAAAMWEFgAAQDGhBQAAUExoAQAAFBNaAAAAxYQWAABAMaEFAABQTGgBAAAUE1oAAADFhBYAAEAxoQUAAFBMaAEAABQTWgAAAMWEFgAAQDGhBQAAUExoAQAAFBNaAAAAxYQWAABAMaEFAABQTGgBAAAUE1oAAADFhBYAAEAxoQUAAFBMaAEAABQTWgAAAMWEFgAAQDGhBQAAUExoAQAAFBNaAAAAxYQWAABAMaEFAABQTGgBAAAUE1oAAADFhBYAAEAxoQUAAFBMaAEAABQTWgAAAMWEFgAAQDGhBQAAUExoAQAAFBNaAAAAxYQWAABAMaEFAABQTGgBAAAUE1oAAADFhBYAAEAxoQUAAFBMaAEAABQTWgAAAMWEFgAAQDGhBQAAUExoAQAAFBNaAAAAxYQWAABAMaEFAABQTGgBAAAUE1oAAADFhBYAAEAxoQUAAFBMaAEAABQTWgAAAMWEFgAAQDGhBQAAUExoAQAAFBNaAAAAxYQWAABAMaEFAABQTGgBAAAUE1oAAADFhBYAAEAxoQUAAFBMaAEAABQTWgAAAMWEFgAAQDGhBQAAUExoAQAAFBNaAAAAxYQWAABAsabruvqdNs2eJP9XvmOez1lJ9k56CFhjHucn10Vd122f9BCnGuvjRHju4FTnMX7yndAauSahxcnVNM39XdddO+k5YC15nAMvhecOTnUe4+uXUwcBAACKCS0AAIBiQuvU8KVJDwAngcc58FJ47uBU5zG+TvkZLQAAgGKOaAEAABQTWgAAAMWE1gbUNM3bmqb5WdM0jzRNc/Ok54FKTdN8pWmaJ5qm2TnpWYCNxfrIqc4aubEIrQ2maZp+ki8keXuSy5P8SdM0l092Kih1Z5K3TXoIYGOxPvIKcWeskRuG0Np4rkvySNd1P++6bpjk75K8e8IzQZmu6+5O8uSk5wA2HOsjpzxr5MYitDaeVyf5xVGfP/6bbQDwSmZ9BNYVobXxNMfY5hr9ALzSWR+BdUVobTyPJ7ngqM/PT/KrCc0CAOuF9RFYV4TWxvOjJL/TNM1rmqbZlOR9Sb474ZkAYNKsj8C6IrQ2mK7rRkn+Msk/Jvlpkm91XffQZKeCOk3TfDPJD5Nc1jTN403TfGjSMwHrn/WRVwJr5MbSdJ3TlwEAACo5ogUAAFBMaAEAABQTWgAAAMWEFgAAQDGhBQAAUExoAQAAFBNaAAAAxf4f/mKcTOvfXBIAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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jr/bNG5c3YGVbwS6ofb5GbMuHEcaLQTohQQ2y8zIqD1fI3pjtd4y0JBEvO09LkjS3OtY+rgIuIk+gbmX6lnoxebIF+QIY7UbaHi99VZFIkl6F2fRonQ/8MKX0ZEqpAXwWeFdHolkOzfc3yVbnyVT17uq+YYStS1qM/dbY1HlZTtOSJHVHZ9rHS8lHdNwB7D6M13vtUZJ6wmwSrROAZ9ueP8c0gxEi4sMRsSEiNqQ0u3lTfX/cR/3jdeqfrBOPFS3DEmi9Jc+eKk9WqH2uRjwbMAbsgcojFWLLRCsSO21RJEldccjt44xHPKXYtgHPA8cCq9v214qyxMSFxkWTjtHf9njPjO8oSeqQ2SzvPl0mMyWTSindSHF3j0qlMusVKiILeAmq/1Sl+dpmfqxVEy+vPFWh8lTbfbUWJ8YuH8tfuyWIYRMtSVJXHHL7GBEHbx/Hk6RjgI9Ms39NUf63wIvkC170AyuYmKfVnpg9f9B3kyR10Gx6tJ4DTmp7fiL5LRMPW/PiJtnpGWl5IlUSaWUie9PE3KvxOVdpRaK1rkVamkjVRLY2o/me5r6GqPKtriyaKL06WQbbtuXb8PBE+fDwRLmk+aD09vGQPND2+GLyeVtnAYNF2SZgy5xFI0maZDY9Wt8BTo+IU8ivjf0C8K9ezZtmZ2Rk52fT72xA9Z58hm9almi9q0VrmolYlQcqVB+pTimXet4zz9B35plTimtXXQVXXQVAY3Ty7HZJPaj09pHHgasnla0Eriweb6LoGyO/VLqO/F5aryu2caPAF19VJJKkV2nGRCul1IyI/w34B/I1jv40pfS9V/Om1e9WyU7L8iGCi8gXvNgJlacrVL9d3dejFbuD+H6Qjk35jYub+XDByncrVL9nkiVJ6p5OtI+HJAPWAxcCZwMD5Dc3fgq4i/2XeZckzbmY7cIVh6JSqaR6faabYM2tUXsIVKJGo9HtEKbV19fX7RB0hBgaGmLDhg1Ogi3ZjHO0uqAT/wdI0pEsIu5LKQ3NVM9JTpIkSZJUMhMtSZIkSSqZiZYkSZIklcxES5IkSZJKZqIlSZIkSSUz0ZIkSZKkkploSZIkSVLJTLQkSZIkqWQmWpIkSZJUMhMtSZIkSSqZiZYkSZIklcxES5IkSZJKVuvEQc877zw2bNjQiUMftuXLl3c7hCl27drV7RDmhUaj0e0Qpujr6+t2CNPyXEm97Q1veEPPtY8R0e0QpkgpdTsESXrV7NGSJEmSpJKZaEmSJElSyUy0JEmSJKlkJlqSJEmSVDITLUmSJEkqmYmWJEmSJJXMREuSJEmSSmaiJUmSJEklM9GSJEmSpJKZaEmSJElSyUy0JEmSJKlktW4H0Ctax7Zo/GSDbHVGWpygCrE3qGyu0HdvH9XN1Ym6J7RonN8gO7ao24TK9gr1h+vUH6p38bvQFFu3Ur3mGuLee4kHHyQaDQCa111HdvnlXQ6uh3ieJM1WH/BRYKB4vgm4sW1/HbgAOKeoMwpsBO4Cts5VkJLUfSZahezojNZprf3K0rJE6/QWe0/dy+K/XEz1hSqt41vsfd9eqLZV7IPsuIzR40bJlmX0f6N/boPXAcWmTVSvv77bYfQ8z5OkWbuYiSRrsgrwQeDktrIasA44DbgJ2NzJ4CSpdzh0sFB5uUL/7f0suXEJS69bypI/XULlheL0VKH52iYAY2eN7Uuyag/VWPpHS1n0hUX7jtN8fXOuQ9dBpIEBWldcQXP9elqXXdbtcHqW50nSrJwIDAGNA+wfYiLJegj4BPA5IAP6gUs7HaAk9Q57tArVTVWqmya6qeKloPa9Go1ji9YkK8pT7KtT+36NGAtqT9byoRH9kGppDqPWjAYHaV17LQDVxx/vcjA9zPMkaSYV8kSpAnwFuGSaOue2Pb4T2As8Rj508FTgeGANsKWTgUpSb5ixRysi/jQitkTEI3MRUC9IkciOzmiuK3qnRqH+SD73qvZIDcby4uYZTVI90Ty1mV+pA2pPmbtK0kKxoNrIC4C1wKPAdNdjqsV+gBFgZ9u+9rlZJ3QkOknqObPJCm4C/gdwc2dD6Q17LttDGpjolYrdwaK/XURle56TVl+ssvhzixl59wjN1zVpvq5IxjKoPVaj/07nZ0nSAnITC6GNXAVcRJ5A3cr0/z0sZmL+8uikfSNtj5eWHp0k9aQZe7RSSl8HXpqDWHpSWpbY+/N7aR2TL5SRrcoYedcIadmkIYIVyFZmZANZF6KUJHXDgmkjLyVfTfAOYPdhvD5mriJJR5rSFsOIiA9HxIaI2LB16/xdv3Xpp5ay9L8uZcn/XEL1ieLS3BJoXJjP1Rp98yhpeZ5k9d3dx9L/tpTFn1kMw5CdkDHyL0ecpyVJ2mfet4+nFNs24HngWGB12/5aUZaA8cV7F7G/9sEeezoTpiT1mtISrZTSjSmloZTS0OrVq2d+QQ+LLKi8XKHvW337yrKj8p6qbNVEj1Xt4RrRDKpbq/vus5WW5fO7JEmCI6B9HE+SjgE+UmwfaNu/pig7HXix7TUr2uq0f9vPdyZMSeo1Lu9eGH3rKM0fa5Itz0iVRDaQMXb+2L79lR35qarsnjhlzXOapFqitbpF67iJe3DFiGMkekaWwbZt+TY8PFE+PDxRLs+TpHI80Pb4YvJ5W2cBg0XZJlxxUNKC4RJ5hebpTcaGxqbf2YC+b+a9W/UNdVontaACjTc3aLx5/5uJVH9UpfKK+WvPeOYZ+s48c0px7aqr4KqrAGiMTp61vQB5niQdyOPA1ZPKVgJXFo83ATcWjyvkNyc+GXhdsY0bBb7YsSglqefMZnn3vwS+CZwZEc9FxL/ufFhzr/5gncpzFWJP5GPMxyC2B7UHaiy5eQnVF/KhgbWnaiz660VUf1TN62Z53cqWCn339LHo7yYPTJckHakWShs5axmwHvgasB1oAsPA94BPA5u7F5okzbUZe7RSSr84F4F0W9+9ffTd2zdzRaD2bI3as3YGzguDg/bEzIbnSTosC6WNnGIHU3u5xo0BXy02SVrAHOMmSZIkSSUz0ZIkSZKkkploSZIkSVLJTLQkSZIkqWQmWpIkSZJUMhMtSZIkSSqZiZYkSZIklcxES5IkSZJKZqIlSZIkSSUz0ZIkSZKkkploSZIkSVLJTLQkSZIkqWS1bgcwV3bt2tXtEKbo7+/vdghTjI6OdjuEKfr6+rodwrzRi+eq0Wh0O4QpevE8Sd2SUup2CFNERLdDmKIXz5Ok3maPliRJkiSVzERLkiRJkkpmoiVJkiRJJTPRkiRJkqSSmWhJkiRJUslMtCRJkiSpZCZakiRJklQyEy1JkiRJKpmJliRJkiSVzERLkiRJkkpmoiVJkiRJJat1OwAdWHZcRuuCFmlNgiVAFRiG2BRUv1Wl8vxEnpxWJFoXtchOyfK6u6HygwrVu6vE3ujWtyAdvq1bqV5zDXHvvcSDDxKNBgDN664ju/zyLgcnqaf0AR8FBornm4Ab2/bXgQuAc4o6o8BG4C5g61wFKWmhMdHqYemYRDoj7V+4HNKZieZpTWo316hsrpCOSox9aCxPsMYNQDaUkZ2SUf9MnRgx2dL8Eps2Ub3++m6HIWk+uJiJJGuyCvBB4OS2shqwDjgNuAnY3MngJC1UDh3sYfFyUP1Slfr1deqfqFO/oU5sKhKmKmTrMgCab23uS7Kqt1epX1un+vVqXnA0tC5qdSF66dVJAwO0rriC5vr1tC67rNvhSOpVJwJDQOMA+4eYSLIeAj4BfA7IgH7g0k4HKGmhMtHqYZXnKlQfqhKvBNEKYntQeaTtR5bnWaTBoterCZX7K8RYUPnORL3s7IzEpJ4xqdcNDtK69lqy974X1q7tdjSSelGFPFGqAF85QJ1z2x7fCewFHiMfOghwPLCmQ/FJWtBmTLQi4qSI+GpEPBYR34uI35yLwLS/FIl0TCI7u8iuRqH6UNFrVZ/hxYuAozoZnSQtPLaPPeACYC3wKPD4NPurxX6AEWBn2772uVkndCQ6SQvcbOZoNYF/l1K6PyKWA/dFxB0ppUc7HJsKjcsbsLKtYBfUPl8jtuXDCOPFIJ2QoAbZeRmVhytkb8z2O0ZakoiXnaclSSWyfeymVcBF5AnUrUz/H81i8mQL8gUw2o20PV5aenSSNHOPVkppc0rp/uLxLvIOd6/9dNNyaL6/SbY6T6aqd1f3DSNsXdJi7LfGps7LcpqWJJXK9rHLLiUf0XEHsPswXu+1R0kddkhztCJiEPgJ4NvT7PtwRGyIiA1bt7pWapn6/riP+sfr1D9ZJx4rWoYl0HpLnj1VnqxQ+1yNeDZgDNgDlUcqxJaJViR22qJIUqfYPs6xU4ptG/A8cCywum1/rShLTFxoXDTpGP1tj/d0JkxJC9usl3ePiGXA3wBXppR2Tt6fUrqR4q4VQ0NDrrxQssgCXoLqP1VpvrYJQFo1cZorT1WoPNV2X63FibHLx/LXbgli2ERLkjrB9rELxpOkY4CPTLN/TVH+t8CL5Ate9AMrmJin1Z6YPd+ZMCUtbLPq0YqIOnkj8hcppS90NiSNa17cJDs9Iy1PpEoirUxkb5qYezU+5yqtSLTWtUhLE6mayNZmNN/T3NcQVb7l4pKah7IMtm3Lt+HhifLh4YlyqctsH+eBB9oeX0w+b+ssYLAo2wRsmeOYJC0IM/ZoRUQA/xN4LKX0XzsfksZlZ2Rk52fT72xA9Z58hm9almi9q0VrmolYlQcqVB+pTimXet4zz9B35plTimtXXQVXXQVAY3Ty7HZp7tg+dtHjwNWTylYCVxaPN1H0IZJfUl5Hfi+t1xXbuFHgix2LUtICN5uhgxcAvwQ8HBHj14V+N6V0a+fCEkD1u1Wy07J8iOAi8gUvdkLl6QrVb1f39WjF7iC+H6RjU37j4mY+XLDy3QrV75lkSVKH2D7OBxmwHrgQOBsYIL+58VPAXey/zLsklWjGRCuldA+uzdMV1W9WqX5z5kQpdgb1z890My1pnhkctMdKPc32scfsYGov17gx4KvFJklzxMk7kiRJklQyEy1JkiRJKpmJliRJkiSVzERLkiRJkkpmoiVJkiRJJTPRkiRJkqSSmWhJkiRJUslMtCRJkiSpZCZakiRJklQyEy1JkiRJKpmJliRJkiSVzERLkiRJkkpW63YAc2X37t3dDmGK0dHRbocwxTnnnNPtEKa47777uh3CFI1Go9shTGvZsmXdDmGKXjxXfX193Q5B0kGklLodwhQR0e0QpujF8yRpgj1akiRJklQyEy1JkiRJKpmJliRJkiSVzERLkiRJkkpmoiVJkiRJJTPRkiRJkqSSmWhJkiRJUslMtCRJkiSpZCZakiRJklQyEy1JkiRJKpmJliRJkiSVrNbtAHpd9bbbqN1yC9X77iO2bIGxMbLXvIbWJZfQuPJKWLWq2yHOqeGjhtn62q2MDIzQ6m+RKonqaJXFLy1m9ROrWfLSEgAaSxu8cM4LjAyM0FzUJKtmVBtVFu9YzNE/PJrlLyyfm4C3bqV6zTXEvfcSDz5INBoANK+7juzyy+cmhjZ+nmbH8yRp3usDPgoMFM83ATe27a8DFwDnFHVGgY3AXcDWuQpSUieZaM2gfuON1O68c7+y6mOPUX3sMWq33MLwPffAwMABXn3kGV0xyq7jd+1X1lzcZNcJu9h13C5O/eqpLHl5CY0lDXaeuHO/eq1FLXYfu5vdx+7mpG+cxMCmzp+32LSJ6vXXd/x9ZsvP0+x4niTNexczkWRNVgE+CJzcVlYD1gGnATcBmzsZnKS54NDBmfT10bjsMobvvpvdW7cy/JWvkJ1wAgCVjRup33xzlwOcW327+zhhwwmccesZnPWFszjtH05j8UuL850VeOU1rwBQG6lx3HeP4/TbT+esL5zFGbeewbLNy/YdZ8fJO+Yk3jQwQOuKK2iuX0/rssvm5D0Pys/T7HieJM1nJwJDQOMA+4eYSLIeAj4BfA7IgH7g0k4HKGku2KM1g5FPfxqWTwxzy974RsZ+/dfp/73fA6Dywx92K7SuWLp9KUu3L933fNGuRax8eiV7V+0FILLYV75o16J99fqG+1j15Cp2H7c7r5dibgIeHKR17bUAVB9/fG7e8yD8PM2O50nSvFUhT5QqwFeAS6apc27b4zuc9GVqAAAgAElEQVSBvcBj5EMHTwWOB9YAWzoZqKROm7FHKyIWRcS9EfFgRHwvIv5gLgLrGcunmUs0OrrvYXbiiXMYTG9JkRhZPrKvd6oyVmHlxpVT65FoLGnw0qkvAXkytupHC3SOjZ+n2fE8aZ5Y8G2kproAWAs8Ckx3fa9a7AcYAdpH2bfPzTqhI9FJmkOz6dEaBX4qpbQ7IurAPRFxW0rpWx2OrSfFCy9Qv+EGANKSJTR/8Re7HFF3PPGzTzC2dGzf89reGq/5xmv268UCePItTzK8enjf88pYhZO+fRLLti5Dfp5my/OkHmYbqQmrgIvIE6hbmf6/rMXkyRbkn552I22PlyJpnpuxRyvldhdP68WWOhpVj4rnnmPxO95BZcsWUqXCyA03kLyyDuQLYjx94dOMDIwctF5Wz3jmJ59h95rdB623EPh5mh3Pk3qZbaT2cyn5J+AO4HCauTkaVS9pbsxqMYyIqEbEA+Sjhe9IKX17mjofjogNEbFh69Yjb13SeOIJFv/0T1P5/vdJtRqjn/oUrXe/u9thdc2Zt53Jur9Zx+m3n86K51YA0Opv8eK6F/erd+rXTmXd59dxxq1nsOqH+XDBVEu88LoX5jzmXuLnaXY8T5oPZmojj/T2UYVTim0b8DxwLLC6bX+tKEtAqyjbfxBIvhDGuD2dCVPS3JlVopVSaqWUziVfR+f8iDh7mjo3ppSGUkpDq1evnnqQeaxy//0sefvbqTz3HGnJEkY++1ma73tft8PqukhB/+5+Vj828fNuLJu6xFIQ9A33sfZ7a/eVjS6fPF5i4fDzNDueJ80XM7WRR3L7qDbjSdIxwEeK7QNt+9cUZacDL7a9ZkVbnfaPx/OdCVPS3Dmk5d1TSjvIb6U33Ro6R6Tq177G4ne+k9i+nbRqFXu/+EVab397t8Pqms2v38zO43bSWNwgi4zG0gbbzty2b3/fnj4Atrx2CztO2kFjSV5vbNEYW86aWD6pb3ff3AScZbBtW74NT8wVY3h4onwO+XmaHc+T5qOF2EbqMD3Q9vhi8nlbZwGDRdkmXHFQOgLMuBhGRKwGxlJKOyJiMfmfhE90PLIe0ffxjxO78hv0xksvseRtb9tvf+vCC9l7223dCK0rdh6/k+2nb592X6VZYfWj+eW43Wt2M7xueNp6ZLD2kbXT7yvbM8/Qd+aZU4prV10FV10FQGN07nrX/DzNjudJ88VCbyPV5nHg6kllK4Eri8ebgBuLxxXymxOfDLyu2MaNAl/sWJSS5tBsVh08DvhMRFTJ/zT8VUrpS50NS71q1ZOr2HXcLkaXjZL1ZZBBfbjO0q1LOeYHx9C/Ox87sfKZlUQKRpeP0urLB6PX9tZYsn0Jx/zgGBbvWNzNb0OSymIbqUOXAeuBC4GzgQHymxs/Rd4n6lQ+6YgwY6KVUnoI+Ik5iKUnedV8f6ufWM3qJ2aeY7DqqVWseqoH7pU1ODinPVYz8fM0O54nzRcLvY3UDHYwtZdr3Bjw1WKTdEQ6pDlakiRJkqSZmWhJkiRJUslMtCRJkiSpZCZakiRJklQyEy1JkiRJKpmJliRJkiSVzERLkiRJkkpmoiVJkiRJJTPRkiRJkqSSmWhJkiRJUslMtCRJkiSpZCZakiRJklSyWrcDmCvLli3rdgjzwsMPP9ztEKbo7+/vdghTjI6OdjuEaTUajW6HMIW/e5KOBCmlbocwRUR0O4QpevE8Sd1ij5YkSZIklcxES5IkSZJKZqIlSZIkSSUz0ZIkSZKkkploSZIkSVLJTLQkSZIkqWQmWpIkSZJUMhMtSZIkSSqZiZYkSZIklcxES5IkSZJKZqIlSZIkSSWrdTsA6dXKjstoXdAirUmwBKgCwxCbguq3qlSen7iekFYkWhe1yE7J8rq7ofKDCtW7q8Te6Na30H1bt1K95hri3nuJBx8kGg0AmtddR3b55V0OTpJUij7go8BA8XwTcGPb/jpwAXBOUWcU2AjcBWydqyClI4eJlua9dEwinZH2L1wO6cxE87QmtZtrVDZXSEclxj40lidY4wYgG8rITsmof6ZOjCzMZCs2baJ6/fXdDkOS1EkXM5FkTVYBPgic3FZWA9YBpwE3AZs7GZx05HHooOa9eDmofqlK/fo69U/Uqd9QJzYVCVMVsnUZAM23NvclWdXbq9SvrVP9ejUvOBpaF7W6EH1vSAMDtK64gub69bQuu6zb4UiSynYiMAQ0DrB/iIkk6yHgE8DngAzoBy7tdIDSkcdES/Ne5bkK1YeqxCtBtILYHlQeafto53kWabDo9WpC5f4KMRZUvjNRLzs7IzGpZ2yhGBykde21ZO99L6xd2+1oJEllqpAnShXgKweoc27b4zuBvcBj5EMHAY4H1nQoPukINetEKyKqEfHdiPhSJwOSXo0UiXRMIju7yK5GofpQ0WtVn+HFi4CjOhmdpCOR7aN63gXAWuBR4PFp9leL/QAjwM62fe1zs07oSHTSEetQ5mj9Jvm1jRUdikV6VRqXN2BlW8EuqH2+RmzLhxHGi0E6IUENsvMyKg9XyN6Y7XeMtCQRLy/MeVqSDpvto3rXKuAi8gTqVqb/z28xebIF+QIY7UbaHi8tPTrpiDarHq2IOBF4B/DpzoYjlWg5NN/fJFudJ1PVu6v7hhG2Lmkx9ltjU+dlLdxpWpIOg+2jet6l5CM67gB2H8brvfYoHbbZDh38b8Bvs+/f1Kki4sMRsSEiNmzd6hqgmnt9f9xH/eN16p+sE48VLcMSaL0lz54qT1aofa5GPBswBuyByiMVYstEKxI7bVEkHRLbR/WuU4ptG/A8cCywum1/rShLTFxoXDTpGP1tj/d0JkzpSDXj0MGIeCewJaV0X0S89UD1Uko3UtyNYWhoaIGuKKBuiyzgJaj+U5Xma5sApFUTH8fKUxUqT7XdV2txYuzysfy1W4IYNtGSNDu2j+p540nSMcBHptm/pij/W+BF8gUv+skHwY7P02pPzJ7vTJjSkWo2PVoXAD8XERuBzwI/FRHrOxqVdAiaFzfJTs9IyxOpkkgrE9mbJi4uj8+5SisSrXUt0tJEqiaytRnN9zT3NUSVby3gRTizDLZty7fh4Yny4eGJckmT2T7qyPFA2+OLyedtnQUMFmWbgC1zHJM0z83Yo5VS+hjwMYDiit2/Tyl9sMNxSbOWnZGRnX+AUTsNqN6Tz/BNyxKtd7VoTTMRq/JAheoj1SnlC8Yzz9B35plTimtXXQVXXQVAY3TyDGlpYbN9VM97HLh6UtlK4Mri8SaKvlbyS+/ryO+l9bpiGzcKfLFjUUpHrENZdVDqSdXvVslOy/IhgovIZ0rshMrTFarfru7r0YrdQXw/SMem/MbFzXy4YOW7FarfW8BJliRJGbAeuBA4Gxggv7nxU8Bd7L/Mu6RZOaREK6V0F/mvm9Qzqt+sUv3mzIlS7Azqn5/pZloL1OCgPVbSq2D7qHljB1N7ucaNAV8tNkmv2gKelCJJkiRJnWGiJUmSJEklM9GSJEmSpJKZaEmSJElSyUy0JEmSJKlkJlqSJEmSVDITLUmSJEkqmYmWJEmSJJXMREuSJEmSSmaiJUmSJEklM9GSJEmSpJKZaEmSJElSyWqdOGhKiUaj0YlDH7a+vr5uhzAv9NrPDWB0dLTbIUwxODjY7RCm9cgjj3Q7hHnBvweSjgQppW6HMEVEdDuEafXiudKRzx4tSZIkSSqZiZYkSZIklcxES5IkSZJKZqIlSZIkSSUz0ZIkSZKkkploSZIkSVLJTLQkSZIkqWQmWpIkSZJUMhMtSZIkSSqZiZYkSZIklcxES5IkSZJKZqIlSZIkSSWrdTuA/WzdSvWaa4h77yUefJBoNABoXncd2eWXdzk4zcif3z6jx4zyyutfoXFUg2xRRqokqqNV+rb2MfDwAP1b+wEYOXaEF3/2xQMeZ82X17D4+cUdjbV6223UbrmF6n33EVu2wNgY2WteQ+uSS2hceSWsWtXR9z8gP0+StDD0AR8FBornm4Ab2/bXgQuAc4o6o8BG4C5g61wFKR26nkq0YtMmqtdf3+0wdJj8+U0YWznG3tfs3a+staTF3pP3svekvRz798fSv62/S9Htr37jjdTuvHO/supjj1F97DFqt9zC8D33wMDAAV7dOX6eJGmBuJiJJGuyCvBB4OS2shqwDjgNuAnY3MngpMPXU4lWGhigdcUVpPPPJ772Naqf+lS3Q9Ih8Oc3obazxtH3HM2izYuoDldpLm+y7c3baKxuQAX2nLpnSqJ1wl+fQG13F34l+/poXHYZzV/+ZbIf/3EqDz/Mol/6JSrPP09l40bqN9/M2G/8xpyH5edJkhaAE4EhoEHeszXZEBNJ1kPAbcAg8F6gH7iU/Xu/pB4yq//qImIjsAtoAc2U0lBHohkcpHXttQBUH3+8I2+hDvLnt8+iLYtgy8Tz+it1lv5oaZ5oAZFFlyKbauTTn4bly/c9z974RsZ+/dfp/73fA6Dywx92JzA/T5on5qyNlI40FfJEqQJ8Bbhkmjrntj2+E9gLPEY+dPBU4HhgDfu1uVKvOJTL5/9rSmlbxyKRjlApEmMrxtjzY3sAiEaw9AdLp9Tb/M7NZP0ZlUaF/i39rHh4RZ6wdVpbkrXP6Oi+h9mJJ3Y+Bmn+s42UDtUFwFrgUeBxpiZa1WI/wAiws23fVvJEC+AETLTUk3pq6KB0pHnuXz5Ha3lr3/PqcJXV/7iavlemjo/IFmf510UZe1+zl70n7mX1V1az5NklcxYvQLzwAvUbbgAgLVlC8xd/cU7fX5K0AKwCLiJPoG5l+v9IF5MnW5AvgNFupO3x1GuXUk+Y7fLuCfhyRNwXER+erkJEfDgiNkTEhm3bvKgnTae1pMWWn95C46h8CGF1b5Wj7j2K475wHCfdfBIn/NUJE71dFXj5/JfnNL547jkWv+MdVLZsIVUqjNxwA8keLWkmB20j29vHrVtdIk0C8iGDdeAOYPdhvL53RuBLBzTbHq0LUkqbImINcEdEPJ5S+np7hZTSjRTTEd/whjekkuOU5qUTP38iKRLN5U12nLeD4VOGyRZl7DhvB2v+cQ31V+rUX6nvq1/ZU+Hofzqa4VOGSbVEc0WTVn+L6mj1IO9SjnjiCRa/+91UnnuOVKsxesMNtN797o6/r3QEOGgb2d4+Dg0N2T5KpxTbNuB54FigfRR7rSgbn/lYBSaPpG9fT2pPxyKVXpVZ9WillDYVX7cAtwDndzIo6UgSKajvrDPw0MTatWMrxgBIHOB/rjn+V6xy//0sefvb8yRryRJGPvtZmu9739wGIc1TtpHSIRpPko4BPlJsH2jbv6YoOx14se01K9rqrG57/HxnwpRerRkTrYhYGhHLxx8DPwM80pFosgy2bcu34eGJ8uHhiXL1Ln9++7x0/ksMv2aY5tImqZIYWzbGK+e8sm9/fVfei7X9wu28fN7LNI5qkCqJ5pIm2y/YTqrnmVZ9R73jvVnVr32Nxe98J7F9O2nVKvZ+8Yu03v72jr7nrPh50jwwp22ktBA90Pb4YvJ5W2eRL/EO+c2NXQhDPSpSOvil84g4lfwKHeSduf9vSuk/Hew1b3jDG9I3v/nNQ49m40b6zjzzoFUao5NnQ85OX990N2fQZI1G4/Bf3KGfXy/+7AYHBw+6f/IiGO1iLFh7+1r6t/Wz5ae2sPfkvdPWowlr7lzD4s2LZx3XI48c+v93i3/2Z6nec88B97cuvJC9t912yMcdd9g/P/8edNXQ0BAbNmxwFsQMDrWNHBoaShs2bJiT2KRuiDjMPxsrgSuLx5uYuDdWBfgQ+9+weNwos75h8Uz/70qHIiLum82tPGaco5VSehJ4fSlRSQvE8ieWs/ekvYytGCPry4gUVHdXWfTCIlZ8bwX1nXmP1orvraA6UmV0zSitJS2yWkZ1pMqizYtY8fAK+naYEEi9zDZS6rAMWA9cCJwNDJDf3Pgp4C7yZd6lHtVby7sPDh72FWr1AH9++ww8PMDAwwMz1lv04iIWvTgH98o6iFfTW9VRfp4kaeHYAVx9gH1jwFeLTZpHZru8uyRJkiRplky0JEmSJKlkJlqSJEmSVDITLUmSJEkqmYmWJEmSJJXMREuSJEmSSmaiJUmSJEklM9GSJEmSpJKZaEmSJElSyUy0JEmSJKlkJlqSJEmSVDITLUmSJEkqWa0TB40I+vr6OnFodVgv/twajUa3Q5ji+9//frdDmNZxxx3X7RCm2L59e7dDmKIXP1O9+LsnSYcqpdTtEKYVEd0OYYpePVcqjz1akiRJklQyEy1JkiRJKpmJliRJkiSVzERLkiRJkkpmoiVJkiRJJTPRkiRJkqSSmWhJkiRJUslMtCRJkiSpZCZakiRJklQyEy1JkiRJKpmJliRJkiSVrNbtAKSO2rqV6jXXEPfeSzz4INFoANC87jqyyy9fUDE11zTZe/5eWke3yBZnUIXYG9RerLHovkXUX6gDsPPnd9I8sXnQY63676s6FmdP68XPkySpfH3AR4GB4vkm4Ma2/XXgAuCcos4osBG4C9g6V0Gq15lo6YgWmzZRvf76boexn27F1Dq6xdipY/uVpWWJsWVjjA2OseLzK6i9OIs/CY0OBTgP9OLnSZLUARczkWRNVgE+CJzcVlYD1gGnATcBmzsZnOYLEy0d0dLAAK0rriCdfz7xta9R/dSnuh1S12Kq7Kiw9M6l1J6tURmukK3I2P0zu2mtbUEVRs8YpfZijRVfWDHltaM/Psqen9kDQP9j/XMSby/qxc+TJKlkJwJD5BcW+6bZP8REkvUQcBswCLwX6AcuZf/eLy1Ys0q0ImIl8GngbCABv5ZS+mYnA5NKMThI69prAag+/niXgyl0Kab65vp+V9iqL1fpf6yf4bXDAEQWB3ztyOtH8gcJFj24qJNh9rZe/Dypq2wfpSNMhTxRqgBfAS6Zps65bY/vBPYCj5EPHTwVOB5YA2zpZKCaD2a7GMYfAbenlH4ceD35x0nSPJUi0VrVYvS1o3lB48A9VWPHjuW9XkD96TrVHdW5ClOaD2wfpSPJBcBa4FFguutp1WI/wAiws21f+9ysEzoSneaZGXu0ImIFcBHwKwAppQYLepaGNL/t+JUdZCuyfc9jd7D875dTfWn6BGr03NF9j/sfWLjDBqXJbB+lI8wq8t/oEeBWpv8veTF5sgX5AhjtRtoeLy09Os1Ds+nROpU8R/+ziPhuRHw6IqZ8fCLiwxGxISI2bN3qcivSfJGWJXb93C6aR09daTBbmtH4sfz/xspLFerP1Oc6PKmX2T5KR5JLyVcTvAPYfRivP/AIfC1Qs0m0asB5wJ+klH4C2AP8zuRKKaUbU0pDKaWh1atXlxympLKsvGklR/2Poxi4eYD6D/LEKS1O7H3T3il1R84Z2XflbtGDiwhbEamd7aN0pDil2LYBzwPHAu2/rrWiLAGtomzylOX2QR97OhOm5pfZJFrPAc+llL5dPP88ecMiaZ6KLKjuqLL4O4v3lbVWtvark6qJ0bPzcRExEgt6tUHpAGwfpSPFeBN3DPCRYvtA2/41RdnpwIttr2lfqLc9MXu+M2Fqfpkx0UopvQA8GxFnFkVvI58iKPW+LINt2/JteHiifHh4onyBxLTnzXtonNqgtaxFqiRaK1qMvGFiQHn1lf3naDVOb5CWJAD6H+0nmvZm9eTnSV1j+ygtUA+0Pb6YfN7WWeRLvEN+c2NXHBQQKaWZK0WcS758bR/wJPCrKaWXD1R/aGgobdiwobQgtbA1Gq9ibvnGjfSdeeZBqzRGJ89m7bAOxnTccccdcN/kRTD2f0NY8YUV1LZMzPx95f2v5KsNZjDwmQGquw5vtcHt27cf1us66bA/Ux382fX1TXezlu4ZGhpiw4YNZtczsH2U5oeIw/hzthK4sni8iYl7Y1WAD7H/DYvHjTLrGxbP5n9w9aaIuC+lNDRTvVndRyul9AD57dkkzVP9j/QzNjhGa2WL1J8gg8quCvXn6yy6f9F+PVr7Len+VP2wkyzpSGf7KC1AGbAeuJD8DnoD5OuNPgXcxf7LvGtBm1WiJc1bg4Nz32M1ky7FtHjDYhZvWDxzRaD+Qp1V/31VhyOah3rx8yRJ6owdwNUH2DcGfLXYpAOY7Q2LJUmSJEmzZKIlSZIkSSUz0ZIkSZKkkploSZIkSVLJTLQkSZIkqWQmWpIkSZJUMhMtSZIkSSqZiZYkSZIklcxES5IkSZJKZqIlSZIkSSUz0ZIkSZKkkploSZIkSVLJat0OQJpJX19ft0OYN7Zv397tEKaIiG6HMEVKqdshSJLmUC/+3e/v7+92CFOMjo52O4QpGo1Gt0M4bPZoSZIkSVLJTLQkSZIkqWQmWpIkSZJUMhMtSZIkSSqZiZYkSZIklcxES5IkSZJKZqIlSZIkSSUz0ZIkSZKkkploSZIkSVLJTLQkSZIkqWQmWpIkSZJUslq3A5CkffqAjwIDxfNNwI1t++vABcA5RZ1RYCNwF7B1roKUJKnzsuMyWhe0SGsSLAGqwDDEpqD6rSqV5yf6S9KKROuiFtkpWV53N1R+UKF6d5XYG936Frpr61aq11xD3Hsv8eCDRKMBQPO668guv3xOQjDRktQ7LmYiyZqsAnwQOLmtrAasA04DbgI2dzI4SZLmTjomkc5I+xcuh3Rmonlak9rNNSqbK6SjEmMfGssTrHEDkA1lZKdk1D9TJ0YWXrIVmzZRvf76rsbg0EFJveFEYAhoHGD/EBNJ1kPAJ4DPARnQD1za6QAlSZo78XJQ/VKV+vV16p+oU7+hTmwqEqYqZOsyAJpvbe5Lsqq3V6lfW6f69WpecDS0Lmp1IfruSwMDtK64gub69bQuu6wrMcyYaEXEmRHxQNu2MyKunIvgJC0QFfJEqQJ85QB1zm17fCewF3iMfOggwPHAmg7FJx2AbaSkTqk8V6H6UJV4JYhWENuDyiNt/7rneRZpsOj1akLl/goxFlS+M1EvOzsjMalnbCEYHKR17bVk730vrF3blRBmHDqYUnqC4l+ciKgCzwO3dDguSQvJBcBa4FHgceCSSfurxX6AEWBn276twKnF4xOALZ0LU5rMNlLSXEiR4Og8aQJgFKoPFb1W9RlevAg4Cni5gwFqWoc6R+ttwI9SSk93IhhJC9Aq4CLyBOpWpv+rtJg82YJ8AYx2I22Pl5YenXQobCMlla5xeQNWthXsgtrna8S2fBhhvBikExLUIDsvo/JwheyN2X7HSEsS8fLCm6fVbYc6R+sXgL+cbkdEfDgiNkTEhq1bXf5L0ixdSn417g5g92G83nZDvWPaNtL2UVKplkPz/U2y1XkyVb27um8YYeuSFmO/NTZ1XtbCnKbVdbNOtCKiD/g54K+n259SujGlNJRSGlq9enVZ8Uk6kp1SbNvIB1wdC7T/+agVZYmJRmLRpGP0tz3e05kwpZkcrI20fZT0avT9cR/1j9epf7JOPFZcXVwCrbfkDWPlyQq1z9WIZwPGgD1QeaRCbJm4Ehk7vSrZDYcydPBngftTSi92KhhJC8x4knQM8JFp9q8pyv8WeJF8wYt+YAUT87Ta/299vjNhSrNgGympYyILeAmq/1Sl+domAGnVxAIXlacqVJ5qu6/W4sTY5WP5a7cEMWyi1Q2HMnTwFznAsEFJ6rgH2h5fTD5v6yxgsCjbhAthqJtsIyWVqnlxk+z0jLQ8kSqJtDKRvWli7tX4nKu0ItFa1yItTaRqIlub0XxPc9/FzMq3FujdnLIMtm3Lt+HhifLh4YnyDptVj1ZELAF+mv+fvXuPs6usD/3/+e69Z5KZXAlJuCRAQC4VBBGjrQWpv4qFKlTb02O1clp7wSq2iqenp+3BVnpOT9V6jtRz6ukhokWLB60itrSAgFyEeg33SwIiRAxJyCQQksncZz+/P9aazM5cMjuwZtZO5vN+vdZr9n7Ws9f+ztrJ/s53P896Nvze9IYjaVZZD1w2pm0xMLI49iZgTX67QvblxMcAp+XbiH7g+mmLUtonc6Sk6VA/sU79tfWJdw5A9e5slag0PzH81mGGJ7gQq3J/herD1XHts8LTT9N+0knjmmuXXgqXXgrAQP/YFbaK1VShlVLqAQ6d1kgkaV/qwNXAWcArgEVkX278FHAH2TLvUgnMkZKmQ/W+KvXj69kUwblkeXAnVH5cofq96p4RregO4vEgHZ6yLy4eyqYLVu6rUH1klhZZLWJ/l3eXpOm1g/GjXCMGgdvzTZKkg1j1O1Wq35m6UIqdQdtXp/oyrVlo1appH7GayiydtClJkiRJ08dCS5IkSZIKZqElSZIkSQWz0JIkSZKkglloSZIkSVLBLLQkSZIkqWAWWpIkSZJUMAstSZIkSSqYhZYkSZIkFcxCS5IkSZIKZqElSZIkSQWz0JIkSZKkgtWm46ApJQYGBqbj0AeV9vb2skOQpl1KqewQxlmwYEHZIYyza9euskOQJM2g/v7+skMYJyLKDmGcVvw7olmOaEmSJElSwSy0JEmSJKlgFlqSJEmSVDALLUmSJEkqmIWWJEmSJBXMQkuSJEmSCmahJUmSJEkFs9CSJEmSpIJZaEmSJElSwSy0JEmSJKlgFlqSJEmSVLBa2QHspauL6kc/Snz/+8QDDxADAwAMXX459YsvNiZJM2L48GEGfmaA+rI6qSNBFaI3qGyu0P79dqqbq6N9Vwwz8NoB6ofnfYegsr1C20NttD3YVuJvIUnSNGsH3g8syu9vAtY07G8DzgROzfv0AxuAO4CumQqyPC1VaMWmTVQ//emyw9hLK8YkaXrVD60zfPzwXm1pfmL4hGF6j+ul45oOqluqDB85TO/be6Ha0LEd6kfU6T+in/r8OnO+PWdmg5ckaaacw2iRNVYFuBA4pqGtBpwCHA9cBWyezuDK11JTB9OiRQx/4AMMXX01wxddVHY4QGvGJGl6VZ6vMOemOXSu6WTe5fPo/FwnlS3522UVhl4+BB70lFYAACAASURBVMDgyYN7iqzagzXmfWoec782d89xhl45NNOhS5I0M1YCq4GBSfavZrTIehD4OPBloA7MAS6Y7gDL19SIVkR8CPhdIAEPAb+VUuorPJpVqxj+xCcAqK5fX/jhX5RWjEnStKpuqlLdNDpMFc8FtUdqDByeZ5N63p5iT5/a4zViMKg9WcumRsyBVEszGLXKMGP5UZJaSYWsUKoAtwHnTdDn9IbbtwK9wDqyqYPHAUcCy4Gt0xlouaYc0YqIFcAHgNUppVeQfX77jukOTJJaQYpE/dA6Q6fko1P90PZwdu1V7eEaDGbNQycOkdoSQ8cNZZ/UAbWnWmp2tgpmfpQ0a50JHAY8Ckw0DlHN9wP0ATsb9jVem7ViWqJrGc3+FVADOiJiEOgku9RNkg5quy/aTVo0OioV3cHcr8+lsj37jKr6bJWOL3fQ97Y+hk4bYui0vBirQ21djTm3en3WLGB+lDS7LAHOJiugbmDiaqKD0euX+8fsaxzzn1d4dC1lyhGtlNIzwP8Ania7ZO2FlNLNY/tFxHsiYm1ErN22bVvxkUpSydL8RO+v9DK8NFsoo76kTt9b+0jzx0wRrEB9cZ36onoJUWqmvJj82NU1C5bZknRwu4BsNcFbgO4X8fiYusvBopmpg4cAbwWOJZtNOS8iLhzbL6W0JqW0OqW0eunSpcVHKkkzbN5n5jHvk/Po/Gwn1cfyj+Y6YeCs7Fqt/tf3kxZkRVb7Xe3M+5t5dHy+A3qgvqJO36/2eZ3WQezF5Mdly5bNdJiSVJxj820b8AxwOND4tlbL2xIwsnjvXPbWONlj9/SE2SqaWXXwHOCplFJXSmkQ+Brws9MbliS1hqgHlecrtH+3fU9b/ZBspKq+ZHTEqvZQjRgKql3VPd+zleZn13fpoGV+lDS7jBRJS4H35tu7GvYvz9tOAJ5teMzChj6Nhdkz0xNmq2im0Hoa+JmI6IyIAN5ItmZI8ep12LYt23p6Rtt7ekbbZ1orxiRpWvW/oZ+hlw1RX1AnVRL1RXUGXzu4Z39lR/bWWekefQsdOnWIVEsMLxtm+IjR7+CKvlk0R2L2mbn8KEkHmvsbbp9Ddt3WycCqvG0TB/WKg9DEYhgppe9FxFeBe4Eh4D72/s7n4jz9NO0nnTSuuXbppXDppQAM9I+9om6atWJMkqbV0AlDDK4enHjnALR/JxvdalvbxvBRw1CBgdcPMPD6vb9MpPqjKpUXWurrClWgGc2PktQK1gOXjWlbDFyS397E6LtghezLiY8BTsu3Ef3A9dMWZctoatXBlNJHgI9McyyS1BLaHmhj6Lgh0iGJNDdBHWJnUP1Jlfa17XtGtGpP1Zj7lbkMrh6kfnid1JFgOPvC49rjNdp+0Fbyb6LpZn6UpEnUgauBs4BXAIvIvtz4KeAO9l7m/SDVWl/ysmpV640OtWJMkqZV+/fbaf9++9QdgdpPatR+0lpvpZIkzZgdjB/lGjEI3J5vs5BzWiRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVzEJLkiRJkgpmoSVJkiRJBbPQkiRJkqSCWWhJkiRJUsEstCRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVrDYdB40I2tvbp+PQUkvo7u4uO4QJzZ8/v+wQxtm+fXvZIYyzefPmskMY59RTTy07hL088cQTZYcgSZphKaWyQxgnIsoO4UVzREuSJEmSCmahJUmSJEkFs9CSJEmSpIJZaEmSJElSwSy0JEmSJKlgFlqSJEmSVDALLUmSJEkqmIWWJEmSJBXMQkuSJEmSCmahJUmSJEkFs9CSJEmSpILVyg5Amk2qN95I7brrqN5zD7F1KwwOUj/6aIbPO4+BSy6BJUvKDrElVO+9l85PfpLqo49S2b4dBgZIS5Yw9OpX0/sHf8DQa14z8zGV/Nr1HNJD18u76FvUx/CcYVIlUe2v0vFcB8seW0bnc50ADMwbYMupW+hb1MfQ3CHq1TrVgSodOzo49IlDWbBlwbTGKUlSqdqB9wOL8vubgDUN+9uAM4FT8z79wAbgDqCr2FAstKQZ1LZmDbVbb92rrbpuHdV166hddx09d98NixZN8ujZo/bYY7TfdNNebfHss7TfcANtN9/MzhtuYOiMM2Y0prJfu/6F/ew6ctdebUMdQ+xasYtdR+ziuNuPo/P5TgY6B9i5cude/YbnDtN9eDfdh3dz1LePYtEm/41Jkg5S5zBaZI1VAS4EjmloqwGnAMcDVwGbiwvFqYPSTGpvZ+Cii+i56y66u7roue026itWAFDZsIG2L3yh5ABbw/Bxx9H9qU/x/L33sv2ZZ9jx7W8zdPrpAMTQEO3XXjvzQZX82rV3t7Ni7QpOvOFETv7ayRz/jePpeK4j21mBF45+AYBaX40j7juCE246gZO/djIn3nAi8zfP33OcHcfsmNY4JUkqzUpgNTAwyf7VjBZZDwIfB74M1IE5wAXFhtNUoRURH4yIhyPikYi4pNgQpNmj78orGfjkJ6mffjrMnUv9Na9h8H3v27O/8sQTJUbXOoZ++qfpf9e7qB99NMyZw/CJJ9L/a7822qE284PxZb9287bP45ANh9De006lXmHurrks/vHiPfujHgDM3TWXQ390KHO651CpV2jvaWfJk6PTGiPFtMY525gfJalFVMgKpQpw2yR9Tm+4fSvQC6wjmzoIcCSwvNiQ9ikiXgFcBLwWeCVwfkScUFwI0iyyYILrY/r799ysr1w5g8EcIIaGqD72GHO+/GUA6vPn0//rvz7zcbTQa5ci0begb8/oVGWwwuINi8f3IzHQOcBzxz0HZMXYkh95HWBRzI+S1ELOBA4DHgXWT7C/mu8H6AMaZ9k3Xpu1oriQmvlY+OXAd1NKPQARcSfwy8BfFxeGNDvFli20XXEFAKmzk6F3vrPkiFrL4le9iupPfrLnfv2ww9j1D//A8EknlRhVpqzX7rFffIzBeYN77td6axz97aOZu2vuXv2e/Lkn6VnWs+d+ZbDCUd87ivld81FhzI+S1AqWAGeTFVA3MHGF00FWbEG2AEajvobb84oLq5mpgw8DZ0fEoRHRCbwZOKq4EKTZKTZupOMtb6GydSupUqHviitIjmjtU+XZZ1nwjndQfeSRUuNopdduqGOIH5/1Y/oW9e2zX72tztM/8zTdy7tnKLJZwfwoSa3gArLVBG8BXkyam6ZZ9VMWWimldWSXit0C3AQ8AAyN7RcR74mItRGxtqur4LURpYNMPPYYHW96E5XHHyfVavR/5jMMv+1tZYfVcnbcdx/bN2/m+e98h/5f+iUAKs89R+dHP1paTGW/difdeBKnXHsKJ9x0Ags3LgRgeM4wz57y7F79jrvzOE756imceMOJLHkimy6Yaoktp22ZsVgPduZHSWoBx+bbNuAZ4HBgWcP+Wt6WgOG8be9JINlCGCN2FxdaU4thpJQ+m1I6I6V0NvAc8MMJ+qxJKa1OKa1etmzZ+INIAqBy7710nnsulY0bSZ2d9H3pSwy9/e1lh9W62tqon3ACvR/60J6myo9+VEoorfLaRQrmdM9h2brR99qB+eOXWAqC9p52DnvksD1t/QvGzpfQS2F+lKSSjRRJS4H35tu7GvYvz9tOAJ5teMzChj6Nb83PFBdas6sOLs9/Hg38CnBNcSFIs0f1zjvpOP98Yvt20pIl9F5/PcPnnlt2WC2n89JLabvhBirPPAMDA1Q2bKDjf/2vPfvrq1bNeExlv3abX7mZnUfsZKBjgHrUGZg3wLaTtu3Z3767HYCtL9/KjqN2MNCZ9RucO8jWk7eO9utun7GYZwPzoyQdQO5vuH0O2XVbJwOr8rZNwFYK0+wayddGxKHAIPD+lNLzxYUgzR7tH/sYsSv70tl47jk63/jGvfYPn3UWvTfeWEZoLaX9hhvoyBeaGCvNm0fPH/3RDEdU/mu388idbD9h+4T7KkMVlj2afRzXvbybnlN6JuxHHQ57+LCJ9+nFMj9KUpnWA5eNaVsMjHzhxiZgTX67QvblxMcAp+XbiH7g+mJDa6rQSim9vtinlaTJ9f3mb9J+yy1Un3yS2LEDajXqK1YweOaZ9F58MfWXvazsEGfckieXsOuIXfTP76feXoc6tPW0Ma9rHkt/uJQ53dncicVPLyZS0L+gn+H2bDJ6rbdG5/ZOlv5wKR07Osr8NQ465kdJOoDUgauBs4BXAIvIvtz4KeAO9l7mvQCRUir2iMDq1avT2rVrCz+u1Cq6u1tz5bb581tv6e7t2ycehSnTnDlzpu40w173uteVHcJennjiCXp7e/1244KZHyVp/0S0ZCq6J6W0eqpOTV2jJUmSJElqnoWWJEmSJBXMQkuSJEmSCmahJUmSJEkFs9CSJEmSpIJZaEmSJElSwSy0JEmSJKlgFlqSJEmSVDALLUmSJEkqmIWWJEmSJBXMQkuSJEmSCmahJUmSJEkFi5RS8QeN6AJ+XMChlgLbCjhOkYypOcbUHGNqXivGdTDHdExKaVkBx1GDAvMjHNz//opkTM0xpuYYU3MO9piaypHTUmgVJSLWppRWlx1HI2NqjjE1x5ia14pxGZPK1IqvtTE1x5iaY0zNMabmlBGTUwclSZIkqWAWWpIkSZJUsFYvtNaUHcAEjKk5xtQcY2peK8ZlTCpTK77WxtQcY2qOMTXHmJoz4zG19DVakiRJknQgavURLUmSJEk64FhoSZIkSVLBWqbQiojzIuKxiHgiIv6k7HgAIuJzEbE1Ih4uO5YREXFURNweEesi4pGI+GALxDQ3Ir4fEQ/kMf1F2TGNiIhqRNwXEf9SdiwAEbEhIh6KiPsjYm3Z8QBExOKI+GpErM//Xb2u5HhOys/PyLYzIi4pM6Y8rg/l/74fjohrImJuC8T0wTyeR1rhHGn6tFqOND82HZP5sUnmx6ZjMkc2F09L5MeWuEYrIqrA48CbgI3AD4B3ppQeLTmus4Fu4AsppVeUGcuIiDgCOCKldG9ELADuAd5W5rmKiADmpZS6I6INuBv4YErpu2XFNCIi/iOwGliYUjq/BeLZAKxOKbXMl/hFxOeBu1JKV0ZEO9CZUtpRdlyw573hGeCnU0pFfcnri4ljBdm/65NTSr0R8Y/ADSmlq0qM6RXAl4DXAgPATcD7Uko/LCsmTY9WzJHmx6ZjMj82H88GzI/7xRw5aTwtkx9bZUTrtcATKaUnU0oDZCfnrSXHRErpW8BzZcfRKKW0OaV0b357F7AOWFFyTCml1J3fbcu30iv4iFgJvAW4suxYWlVELATOBj4LkFIaaKUkArwR+FGZCaRBDeiIiBrQCWwqOZ6XA99NKfWklIaAO4FfLjkmTY+Wy5Hmx6ZjMj8eoA6A/AjmyMm0TH5slUJrBfCThvsbKfnN8UAQEauAVwHfKzeSPVMQ7ge2AreklEqPCfgb4D8D9bIDaZCAmyPinoh4T9nBAMcBXcDf51NIroyIeWUH1eAdwDVlB5FSegb4H8DTwGbghZTSzeVGxcPA2RFxaER0Am8Gjio5Jk0Pc+R+Mj9Oyfw4tVbPj2COnEzL5MdWKbRigrbSP/FpZRExH7gWuCSltLPseFJKwyml04GVwGvzYdvSRMT5wNaU0j1lxjGBM1NKZwC/CLw/n35TphpwBvB3KaVXAbuB0q//AMinafwS8JUWiOUQshGEY4EjgXkRcWGZMaWU1gEfB24hmxbxADBUZkyaNubI/WB+3DfzY9NaNj+COXJfWik/tkqhtZG9K82VlD8tp2Xl87yvBb6YUvpa2fE0yofV7wDOKzmUM4Ffyud8fwn4+Yi4utyQIKW0Kf+5FbiObEpQmTYCGxs+Yf0qWWJpBb8I3JtSerbsQIBzgKdSSl0ppUHga8DPlhwTKaXPppTOSCmdTTaNy+uzDk7myCaZH5tifmxOK+dHMEfuU6vkx1YptH4AnBARx+YV+juAfy45ppaUX1j7WWBdSumTZccDEBHLImJxfruD7D/c+jJjSin9aUppZUppFdm/p9tSSqWOQETEvPwCbfLpB79ANrxdmpTSFuAnEXFS3vRGoNRFaBq8kxaYEpF7GviZiOjM/w++kez6j1JFxPL859HAr9A650vFMkc2wfzYHPNjc1o8P4I5cp9aJT/WynjSsVJKQxHx+8A3gCrwuZTSIyWHRURcA7wBWBoRG4GPpJQ+W25UnAn8B+ChfM43wH9JKd1QYkxHAJ/PV7+pAP+YUmqJ5WJbzGHAddl7EDXg/6WUbio3JAD+APhi/gfck8BvlRwP+ZzqNwG/V3YsACml70XEV4F7yaYf3AesKTcqAK6NiEOBQeD9KaXnyw5IxWvFHGl+bJr5sTnmx/1gjmxKS+THlljeXZIkSZIOJq0ydVCSJEmSDhoWWpIkSZJUMAstSZIkSSqYhZYkSZIkFcxCS5IkSZIKZqElSZIkSQWz0JIkSZKkglloSZIkSVLBLLQkSZIkqWAWWpIkSZJUMAstSZIkSSqYhZYkSZIkFcxCS5IkSZIKZqElSZIkSQWz0JIkSZKkglloSZIkSVLBLLQkSZIkqWAWWpIkSZJUMAstSZIkSSqYhZYkSZIkFcxCS5IkSZIKZqElSZIkSQWz0JIkSZKkglloSZIkSVLBLLQkSZIkqWAWWpIkSZJUMAstSZIkSSqYhZYkSZIkFcxCS5IkSZIKZqElSZIkSQWz0JIkSZKkglloSZIkSVLBLLQkSZIkqWAWWpIkSZJUMAstSZIkSSqYhZYkSZIkFcxCS5IkSZIKZqElSZIkSQWz0JIkSZKkglloSZIkSVLBLLQ0oYh4d0Skhm0gIn4UEX8VEXNLiumyiEhlPPdEIuJtEfGtiNgaEb0R8eOI+HpEnPcijjVtv1tEvCE/fmVM+6r8tX33dDyvJM02EfG6iPhSRGzM8+bOiPhBRPy3iDii7PgaRcSGiLiqoGPNiYgPRcQDEbEr/73XR8TnI+KEF3G8OyLijiJim+DY746I356kPUXEqul4Xs1OFlqayr8HXge8BfgG8KfAJ0qNqAVExAeA64AfAr9Ddn7+Mt/982XFNYk3AB9h/P/3zWSv7b/OdECSdLCJiD8E/g1YBnwYOAd4B1nufA/wufKim3bXAP8NuBb4d8CvAWuAE4GTS4xrIu8GxhVaZLnwdWS5USpErewA1PLuTyk9kd++Jf9k6nci4oMppXqZgZXsPwFfTyn9TkPbbcBnxo4ctaqUUj/w3bLjkKQDXUT8f2QfQn4qpfShMbtviIiPkn1wua9jtAFDKaWWmbnRjIg4Dvhl4JKU0qcadt0IfPIAyoldQFfZcejgckD841dLuRfoAJaONETEsoi4IiIej4ieiPhJRPy/iFjR+MCR6XERcUJE/GtEdOfT7f58gmltr4qIuyKiLyKeiYg/A2JsMBGxMCL+NiI2RUR/RDyWT1+Ihj5vyJ/3bXmcz0XE8xFxeURUI+I1EXF3ROyOiEci4twmzsMSYMtEO8YWoBFxbER8MSK68hjvj4hfnuoJIqIWEX+aT7/oz3/H/zl26mZEzIuIj+VTO/sjYktEXBsRh0XEZWSjWQCDI1NB88dNOHUwIi7Mp3/0RcS2iPiHsVNe8iknV0fEOyJiXX7u1kbEWVP9XpJ0EPpjYFv+c5yU0u6U0lUj9xvefy+OiL+OiE1AP7A4399U3oiIV0bEP+c5rTci/i0iXj9Bvw/m79t9+Xv168fsf3Uez1sneOxVkU2FrE7yuy/JfzabE38uIr4Z2RTD3RHxjYh4xSTHbnzc0oj4u/xvgv48N75ngn7H5nlrS97vyYj4VL7vDuDngDNj9NKIO/J946YORkRbRPxlfu4G8p9/GVlRPNJn5LX8vYj4rxGxOSJ2RMT1EbFyqt9LBzdHtLS/VgEvANsb2pYAfWTTCruAI4E/BP4tIn4qpdQ35hjXAX8PXA5cAPwF8JO8jYhYSjY6tAX4TbLk80fA0Y0Hiaw4+1fgDODPgYfIpvB9kmzqxn8Z87x/A3yNbErD2WRTO2pk0zs+ATyTt30tIo5JKW3bx3n4PvCbEfEk8E8ppccn6hQRRwHfA7YCH8rPz68B10bE21JK/7yP57ia7Px8HPg28HKyqRmryKZmEBHtwC3A6cBHyUaoFgHnAocAVwIryaY3ngUM7+P5yJPWFcCXyV7PI4G/An46Is5IKXU3dH89cBLwZ2Sv/38D/iUiVqWUduzreSTpYBERNbI/3r+WUhrYz4dfCvyAbGphFehrNm9ExBnAXcB9wEVAD/Be4NaI+NmU0j15v98hy39Xkb23H0821W/BSBAppXsi4gfA7wH/1PC7LQbeDvx1Smmy/LEe2Al8LC9AbkkpPTtRx4h4S378fwUuzJv/GLgrIk5LKf1kksctJJuW2QFcBjxFluf+LiLmpJT+d97vWLL83EP2IeMPgaOAX8gPdTFZbq3mvyt57JP5fP77/xVwN9nUwg8DxwG/Pqbvn5Ll6t8GlgP/E/gi2b8NzVYpJTe3cRvZHOZE9od0jeyP9t8GhoDfn+KxVbI3tgT8ckP7ZXnbb43p/xBwc8P9/w4MAEc3tM0j+7QwNbSdnx/v3WOOdyVZcbY0v/+GvN/nxvS7N28/q6HttLztN6f4HU8EHsz7pjy2a4BfGNPvs2RJ8tAx7beQTcvc69w03H99ftzfGPO4d+Xtp+f3fzu//0v7iHXkvNfGtK9qPH/56/YscPuYfmfl/T7Q0LYBeB44pKFtdd7v18v+9+vm5uY2UxtwWP7e99EJ9tUat4b2kfffe4EY85hm88Y3gXVAe0NbNW/7en6/QvZB5k1jjvVr+fNf1dD2brIP445paPsAWd5fOcU5uCCPeSQn/gj4W+CnxvR7AvjmmLaFeQ79m4a2O4A7Gu6PfKB3wpjHfiZ/bC2//wWgGzhyH7HeAdw9Qfu789hX5fdfkd+/bEy/D+ftp415Le8c0+8/5e2TxuJ28G9OHdRU1gODwHNkb/5XpJT+dmyniHhfPt2sm+xN+el810kTHHPs4gsPs/do1euA76aURo5BSmk3cP2Yx50N1MkKnEZXA+35cRrdOOb+emB3SunuMW2QFYqTStkI1qvIPqn678D9ZHPUvxERH27oeh5wA/BCZFMBa/mnn98AXpl/SjeR88iKzWvHPO7mfP/Z+c9fALakfY+MNesksk/hvtjYmJ+fHzP+U7nvpJSeb7j/UP7zaCRp9hg3rR0gIg4ny597tvx9vNHXU0pjr8maMm9ERAfZe/JXgHpDnwBuZTRHrMy3fxzzHNeS5epGXwJ2kI2Ojfg94F9TShsn//UhpXQ9WcHxK8D/zo9zMXBfRJyTn48TgJcBXxzze/UA32mIeSLnkY3yPTXBOTmU0QU3fgH4l5TSpn3F26SReK4e0z5yf2xOHPu3jTlRFlqa0i8DrwHeTPbmfXFE/EZjh4j4A+D/5Pt/BXgt8DP57omWgn9uzP3+Mf2OIBtZGWts2xLguZQt6tBoS8P+Rs+PuT9Algz2SKPTPqZcwj6lNJxS+lZK6cMppXPIphI8BHwkIg7Juy0HfoMxyZbRlRsPneTwy8mKxe4xj9s65nGHkk15LMLI+ZpoxaUtjD+fe72ODa9DKcv/S1JJtpGNtoz9g3obWf58DdnIy0Qmer9tJm8sIRu9+rMJ+v0+cEg+vX7k+tq98mdKaYi9LwEgZdP8/55swatafh3XycD/nST2vaTsOrTrUkofSCm9GvhZshGyjzX8XpB9aDs25vOZPB+OPPbsCR73lXx/Y07cZ1G4HybLiZP9jTHR3zZgTpzVvEZLU3k45asORsRtZNPlPhER1+ajTJAtX/vNlNIfjjwonyf9Ym0mm4ox1ti254AlEdGe9p4Xf3j+czszKKW0KSKuBD4FnEA2T3w72Rz6j0/ysMk+ddtOlrjHXdQ85nHbyKY3FGEkSRw+wb7DgbUFPY8kHTRSSkMR8S3gTY35KC9m1gJExPmTPXyCtmbyRo1sRsenyabLTRRXPSJGioS98mc+GjRRYfN3wH8E3kr2QesGslGj/ZZS+m5E3Ew2GgWjOflPyT6YHWtf17dtJ/ug8YOT7H8s/7kNWDFJn/3VmBN/1NBeyt8YOjBZaKlpKaX+iPgjsgtZL2b007VOxl9M+lsv4am+A/xRRByV8gtjI2Ie2RzwRneSLZLx79l7utu7yN6wp23p8sbYxvip/OfIJ143kU1hfCSl1LsfT3ET2QXCi1JK39xHv5uBd0TEBfnUjYmMfKrWAezax7EeI/vU8x1knzgCEBE/CxxDdmGvJGm8vya7hurjZAtYvBTN5I3+iLgLeCVwb5r861Y2kl2j9Xb2/h6vf8cEfwOmlH6UF0d/RLbI0n/dx7EBiIgFZNeZ7RzTXiX70HGk2HuMrHA7JaX0MfbPTcAfAE+nlLbuo9/NwK9ExBEppcm+D6ufhoVA9uHO/Oc7yC4RGPGu/Oe3mjiGZjkLLe2XlNI/5ysT/aeI+Ns8CdwE/HFE/BeyUZyfB371JTzN5WSF3M2RLU8+surg2IRzI9kqQP83IpYBj5BNcfxdsouS97Vq4Ev1cETcTraC4lNkF/O+mWzFp39suL7sz8nOybci4m/JkswhZKNQx6WUJvrSRFJKd0TENcBXI+KT+THqZHPg3wz8cX6d2NVk8+mviex7Wr5HlkDOJbuweD3waH7YP4yIG4HhlNK40amU0nBE/DlwRURcnR97BVmC+SH5qpCSpL2llL4ZEX9CtvLeaWSjTE+RTRs7keyP9d1MPII1VrN54z+S/bH/jYj4LFlBs5RsJd5qSulP8lGtvwCujIi/J7sO63iyUaXJVtv7P2QfqA7S3JcsnwTcHhH/QLZAx1ayKYu/m8d8MWSrPUXE+4F/ylfM/UeyEajDyKYZPp1S+uQkz3E52QIed0XE5WRF2zyyDzdfn1IaWZb+I2SrD387Iv6KbPGNFcB5KaWRVQ4fJbsM4tfIRqp2pZQeY4yU0iN5Hr4sHwH8kS4sMwAAIABJREFUNlkB/GfANSmlB5s4N5rtyl6Nw601N0ZX3zl+gn2/kO/7UH6/g2y6QRfZiMm/AMcyZrUeJl/97ipgw5i2kWVr+8iuQfozsmXg05h+C8lWNtpMNor1ONmnidHQ5w35854zwfNunOD3S8BfTnF+3gv8M9kiEX1kCfQ+4D/TsAJU3ncl2UqIz+Qxbib75PPCsedmzOMqZNMkHsif44X89l+TjXSN9JtPNrr444bjfxVYnu+vkk0v2UpWrKW8fRUTr9p4Yf48/WRTI/4BOGJMnw3A1ZOcu8v2de7c3NzcDtYNOJOsgBh5v99Jtnz7XzS+jza8//7uJMeZMm/k/V5OVjxtzd+zN+a56c1j+n2wIV+tJVtNdgMNqw429K3mOe0rTf7Oi8mKw2/lcQ6SXRN9O/CrE/R/HdnfCc/n8WzIf4fXNfS5g4ZVB/O2Q8gKrqfyc7KV7O+ES8b0exnZIlnb8nPyJHB5w/7DyRYb2ZW/Bnfk7e+mYdXBvK0N+Mv83A3mP/8SaJvqtWT0b483lP3v0q28LfJ/DJIkSZrlIuJNZFPwzkn7nrouaQoWWpIkSbNcRLyMbPXcy4H+lK0cKOklcHl3SZIk/RnZtc/9ZMvLS3qJHNGSJEmSpII5oiVJkiRJBZuW5d0jouWGyV79aqcaS1KzNmzYwLZt26LsOA425kdJOvDdc88921JKy6bqNy1TB1sxkThFUpKat3r1atauXWuhVTDzoyQd+CLinpTS6qn6OXVQkiRJkgpmoSVJkiRJBbPQkiRJkqSCWWhJkiRJUsEstCRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVzEJLkiRJkgpmoSVJkiRJBauVHQAA7cD7gUX5/U3Amob9bcCZwKl5n35gA3AH0DVTQUqSNMPMj5J0wGqNQuscRpPIWBXgQuCYhrYacApwPHAVsHk6g5MkqSTmR0k6YJU/dXAlsBoYmGT/akaTyIPAx4EvA3VgDnDBdAcoSVIJzI+SdEBrqtCKiPMi4rGIeCIi/qTQZ78g/3nbJH1Ob7h9K9ALrCObGgFwJLC8sIgkSWqa+VGSNJkpC62IqAKfBn4ROBl4Z0ScXMiznwkcBjwKrJ9gfzXfD9AH7GzY1zj3fEUh0UiS1DTzoyRpX5oZ0Xot8ERK6cmU0gDwJeCtL/mZlwBnkyWIGybp00GWTCC7wLdRX8PteS85GkmS9pf5UZI0qWYKrRXATxrub6SIz8guIFst6Rag+0U8Pl5yBJIkvRTmR0nSpJpZdXCit+w0rlPEe4D3NPWsx+bbNuAZ4HBgwZioDgd2AcNkn9rNHXOMOQ23dzf1rJIkFcn8KEmaVDOF1kbgqIb7K8m+yWMvKaU15N/uERHjEs1eRpLAUuC9E+xfnrd/HXiW7ILeOcBCRuehL2vo/8wUv4EkScUzP0qSJtXM1MEfACdExLER0Q68A/jn6Q2rwf0Nt88hm5d+MrAqb9sEbJ2xaCRJGmF+lCRNasoRrZTSUET8PvANskkKn0spPfKSnnU9cNmYtsXAJfntTeSf/ZGVgqeQfVfIafk2oh+4/iVFIknSi2J+lCTtSzNTB0kp3cDkax9NrzpwNXAW8ApgEdmXNz4F3MHey9hKkjSDzI+SpMlESvueLv6iDjrVHPQSTMfvKUkHq9WrV7N27VrXryuY+VGSDnwRcU9KafVU/Zq5RkuSJEmStB8stCRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVzEJLkiRJkgpmoSVJkiRJBbPQkiRJkqSCWWhJkiRJUsEstCRJkiSpYBZakiRJklQwCy1JkiRJKlhtOg766le/mrVr107HoV+0I444ouwQxtm8eXPZIRwQuru7yw5hnPnz55cdwoQGBgbKDmGc9vb2skMYx/OksrRifoyIskMYJ6VUdgiS9JI5oiVJkiRJBbPQkiRJkqSCWWhJkiRJUsEstCRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVzEJLkiRJkgpmoSVJkiRJBbPQkiRJkqSCWWhJkiRJUsFqZQfQKgaXDdJ9RjdDhw5R76iTKolKX4W2rW3Mu38e7c+279V/aNEQ3Wd0M7BigHpHnRgIai/U6Hykk44fdpT0W6hR9cYbqV13HdV77iG2boXBQepHH83weecxcMklsGRJ2SG2jq4uqh/9KPH97xMPPEAMDAAwdPnl1C++uOTgWojnSYJ24P3Aovz+JmBNw/424Ezg1LxPP7ABuAPomqkgJal8Flq5wSWD9B/bv1dbfV6d/mP76T+6nyX/tIT2rVmx1b+inx3n7SC1pT19U0disGOQ/p39Flotom3NGmq33rpXW3XdOqrr1lG77jp67r4bFi2a5NGzS2zaRPXTny47jJbneZKAcxgtssaqABcCxzS01YBTgOOBq4DN0xmcJLUOpw7mai/UWHj7QpZ+cSmHrTmMpV9aSm1rXodWoe/4PgDqc+rsOCcrsqI/WPTNRSz/++Us/9xylvzTEuZumFvib6G9tLczcNFF9Nx1F91dXfTcdhv1FSsAqGzYQNsXvlBygK0jLVrE8Ac+wNDVVzN80UVlh9OyPE+a9VYCq4GBSfavZrTIehD4OPBloA7MAS6Y7gAlqXU4opVr39JO+5bR6YG1HTU6Hu9g1/JdWUM9+9H7U72kjmwka8F3Fuw1etW+ee/phSpX35VXwoIFe+7XX/MaBt/3PuZ8+MMAVJ54oqzQWs+qVQx/4hMAVNevLzmYFuZ50mxWISuUKsBtwHkT9Dm94fatQC+wjmzq4HHAkcByYOt0BipJrWHKEa2I+FxEbI2Ih2cioFaQIjF0yBC9J/YCEANB52OdQDZtcMTQ4iG63tHFlt/dQtevd7Fr9S5SJU14TJWgocjao3/09auvXDmDwUg6GM2qHHkmcBjwKDDR5wzVfD9AH7CzYV/jtVkrpiU6SWo5zYxoXQX8LTAr5lltfddW6gvqe+5XdldY/I3F1J7PTlV9/ui+ntN79tweXjjM7tW7GV48zOJbF89cwGpabNlC2xVXAJA6Oxl65ztLjkjSQeAqZkOOXAKcTVZA3cDEfz10kBVbkC2A0aiv4fa8wqOTpJY05YhWSulbwHMzEEtLqs+r8/wvPs/gkkGAvUasal01ln1hGUuvWUplV3Yq+47vY3DpYCmxanKxcSMdb3kLla1bSZUKfVdcQXJES9JLNGty5AVkqwneAnS/iMdHseFI0oGgsMUwIuI9EbE2ItZ2dR2467cu/+LybDGMa5Yy50dzgGxFwe7XZJml0jd6yjoe66DaU6X2Qo25T40ugmGh1VriscfoeNObqDz+OKlWo/8zn2H4bW8rOyxJs8QBnx+PzbdtwDPA4cCyhv21vC0Bw3nb2HWh5jTc3j09YUpSqyms0EoprUkprU4prV62bNnUD2hhUc++E2v+vfP3tA0tHgKgratt6scP+dFdq6jcey+d555LZeNGUmcnfV/6EkNvf3vZYUmaRQ74/DhSJC0F3ptv72rYvzxvOwF4tuExCxv6NP7az0xPmJLUalzePbfzZ3fSt6qP4XnDpEpiaMEQu08f/dittjObkN7x+Ogqg70n9TLcOczQwiH6js0noA+7+mCrqN55Jx3nn09s305asoTe669n+Nxzyw6rNdXrsG1btvWMXntIT89ouzxP0lTub7h9Dtl1WycDq/K2TbjioKRZw+Xdc33H9tFzWs+E+2IwmL82G91q62qj84FOel7Zw9CyIbp+Y+9pIPPvmU91d3Wiw2iGtX/sY8SubHn+eO45Ot/4xr32D591Fr033lhGaK3n6adpP+mkcc21Sy+FSy8FYKB/7NXts5DnSbPReuCyMW2LgUvy25uANfntCtmXEx8DnJZvI/qB66ctSklqOc0s734N8B3gpIjYGBG/M/1hzbzORztp29xGpaeSzTEfhOrzVToe6eDQrx6615TBhd9ZyMI7FlLrqsFQVoi1bW5j8c2L95puKEk6uM2WHNm0OnA1cCewHRgCeoBHgCuBzeWFJkkzbcoRrZTSrFgDe/5985l/X/NFUuf6TjrXd05jRHqpHK3aD6tWORLTDM+TxpgtOXKcHYwf5RoxCNyeb5I0i3mNliRJkiQVzEJLkiRJkgpmoSVJkiRJBbPQkiRJkqSCWWhJkiRJUsEstCRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVzEJLkiRJkgpmoSVJkiRJBbPQkiRJkqSC1coOYKZs3ry57BDGiYiyQxgnpVR2COPMnz+/7BAOGO3t7WWHcEDwPEmjWvF93/wo6WDgiJYkSZIkFcxCS5IkSZIKZqElSZIkSQWz0JIkSZKkglloSZIkSVLBLLQkSZIkqWAWWpIkSZJUMAstSZIkSSqYhZYkSZIkFcxCS5IkSZIKZqElSZIkSQWrlR2A9kM78H5gUX5/E7CmYX8bcCZwat6nH9gA3AF0zVSQkiTNMPOjpBZkoXUgOYfRJDJWBbgQOKahrQacAhwPXAVsns7gJEkqiflRUgty6uCBYiWwGhiYZP9qRpPIg8DHgS8DdWAOcMF0ByhJUgnMj5JalIXWgaBClggqwG2T9Dm94fatQC+wjmxqBMCRwPJpik+SpDKYHyW1sCkLrYg4KiJuj4h1EfFIRHxwJgJTgzOBw4BHgfUT7K/m+wH6gJ0N+xrnnq+YlugkaVYyP7YA86OkFtbMNVpDwB+mlO6NiAXAPRFxS0rp0WmOTQBLgLPJEsQNTPyKdZAlE8gu8G3U13B7XuHRSdJsZn4sk/lRUoubckQrpbQ5pXRvfnsX2YC7n/3MlAvIVku6Beh+EY+PYsORJGXMjyUzP0pqcft1jVZErAJeBXxvgn3viYi1EbG2q8u1UgtxbL5tA54BDgeWNeyv5W0JGM7b5o45xpyG27unJ0xJmu3MjzPM/CjpAND08u4RMR+4FrgkpbRz7P6U0hryb61YvXp1KizC2WwkCSwF3jvB/uV5+9eBZ8ku6J0DLGR0Hnpj4nlmesKUpNnM/FgC86OkA0BTI1oR0UaWRL6YUvra9IakF+X+htvnkM1LPxlYlbdtArbOcEySdJAzPx4AzI+SSjLliFZEBPBZYF1K6ZPTH5L2WA9cNqZtMXBJfnsT+WekZCXzKWTfFXJavo3oB66ftiglaVYyP5bI/CjpANDMiNaZwH8Afj4i7s+3N09zXNpfdeBq4E5gO9laWD3AI8CV+K33klQ88+OBwPwoqSRTjmillO7GtXlaxw7Gf4o3YhC4Pd8kSdPK/NhizI+SWsx+rTooSZIkSZqahZYkSZIkFcxCS5IkSZIKZqElSZIkSQWz0JIkSZKkglloSZIkSVLBLLQkSZIkqWAWWpIkSZJUMAstSZIkSSqYhZYkSZIkFcxCS5IkSZIKZqElSZIkSQWrTcdBU0oMDAxMx6FftPb29rJDGCelVHYI46xatarsEMbZsGFD2SHoJeju7i47hHHmz59fdgjjtNp5qtfrZYegWawV82NElB3COK14niSNckRLkiRJkgpmoSVJkiRJBbPQkiRJkqSCWWhJkiRJUsEstCRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVzEJLkiRJkgpmoSVJkiRJBbPQkiRJkqSC1coOYC9dXVQ/+lHi+98nHniAGBgAYOjyy6lffHHJwQmgf2k/L7zyBQYOGaA+t06qJKr9Vdq72ln00CLmdM0BoO/wPp79xWcnPc7ym5fT8UzHTIWtA0z1xhupXXcd1XvuIbZuhcFB6kcfzfB55zFwySWwZEnZIbYEz5PUwtqB9wOL8vubgDUN+9uAM4FT8z79wAbgDqBrpoKUNJ1aqtCKTZuofvrTZYehfRhcPEjv0b17tQ13DtN7TC+9R/Vy+L8ezpxtc0qKTgeLtjVrqN16615t1XXrqK5bR+266+i5+25YtGiSR88eniephZ3DaJE1VgW4EDimoa0GnAIcD1wFbJ7O4CTNhJaaOpgWLWL4Ax9g6OqrGb7oorLD0QRqO2scevehrPjKCo7+/NEc+bUjae9qz3ZWYPdxu8c9ZsVXVnDM3x+z1+ZolvapvZ2Biy6i56676O7qoue226ivWAFAZcMG2r7whZIDbBGeJ6k1rQRWAwOT7F/NaJH1IPBx4MtAHZgDXDDdAUqaCS01osWqVQx/4hMAVNevLzkYTWTu1rmwdfR+2wttzPvRPAaWZdkk6lFSZDqY9F15JSxYsOd+/TWvYfB972POhz8MQOWJJ8oKraV4nqQWVCErlCrAbcB5E/Q5veH2rUAvsI5s6uBxwJHAcvbKt5IOPFMWWhExF/gW2WcsNeCrKaWPTHdgan0pEoMLB9n9smwUKwaCeT+cN67f5vM3U59TpzJQYc7WOSx8aGFWsEmTaSge9ujv33OzvnLlDAbTwjxPpTNHapwzgcOAR4H1jC+0qvl+gD5gZ8O+LrJCC2AFFlrSAa6ZEa1+4OdTSt0R0QbcHRE3ppS+O82xqYVt/NWNDC8Y3nO/2lNl2TeX0f5C+7i+9Y569nNund6je+ld2cuy25bR+ZPOGYtXB7bYsoW2K64AIHV2MvTOd5YcUWvyPJXCHKlRS4CzyQqoG5j4r6wOsmILsn89jfoabo//3FLSAWbKQiullIDu/G5bvqXpDEoHnuHOYba+aSuH3XQY7c+3U+2tcsj3D2HuxrnUumvU59bZ8aod7D5hN1Tg+dc+b6GlpsTGjXS89a1Utm4lVSr0XXEFyZGacTxP5TBHai8XkP0LuInsX8Xi/Xy8s++lg0pTi2FERDUi7icbxL4lpfS9Cfq8JyLWRsTabdu2FR2nWszKr67k6KuO5shrj6Tzqaxgqs+ts+OMHUB27dbCRxbS/kI7leEKtd01Dv23Q4mhLIsMLRxieM7wpMeXAOKxx+h405uoPP44qVaj/zOfYfhtbys7rJbjeSrXVDmyMT92dblu90Hr2HzbBjwDHA4sa9hfy9sSMJL+xs6ib1y0d/zaUpIOME0VWiml4ZTS6WTr6Lw2Il4xQZ81KaXVKaXVS5cuLTpOtaBIQdvONhY9OLp+7eDCQQDSZB/o+jmvmlS59146zz2XysaNpM5O+r70JYbe/vayw2o5nqfyTZUjG/PjsmXLJj6IDnwjRdJS4L359q6G/cvzthOAZxses7ChT+M/j2emJ0xJM2e/lndPKe0g+yq9idbQeenqddi2Ldt6ekbbe3pG21Wq5177HD1H9zA0b4hUSQzOH+SFU1/Ys79tVxsA28/azvNnPM/AIQOkSmKoc4jtZ24ntWWVVtuONqr91QmfQ6reeScd559PbN9OWrKE3uuvZ/jcc8sOq+V4nlrLtOdIHTzub7h9Dtl1WycDq/K2TbgQhnQQaGbVwWXAYEppR0R0kL0lfHxaonn6adpPOmlcc+3SS+HSSwEY6B975ahmUs/RPew6ZdeE+2IwWHR/NrpVb6+z+4Td7HzlzvEdh+CQ7x4ynWHqANf+sY8Ru7J/Z/Hcc3S+8Y177R8+6yx6b7yxjNBaiuepfDOaI9Xa1gOXjWlbDFyS394ErMlvV8i+nPgY4LR8G9EPXD9tUUqaQc2sOngE8PmIqJK9NfxjSulfpjcstaoFjy2g96heBhcOUm+vEymodleZu2UuCx9ZSNvObERr4SMLqfZV6V/ez3DnMPVanWpflbmb57LwoYW07xi/OqEkHYDMkdp/deBq4CzgFcAisi83fopsTNRL+aSDQjOrDj4IvGoGYoFVqxyxanGLHlrEoocWTdlv7rNzmfus35WlF8dRmOZ4nso3ozlSB54djB/lGjEI3J5vkg5K+3WNliRJkiRpahZakiRJklQwCy1JkiRJKpiFliRJkiQVzEJLkiRJkgpmoSVJkiRJBbPQkiRJkqSCWWhJkiRJUsEstCRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVrDYdB40I2tvbp+PQmmYbNmwoO4RxIqLsEMZJKZUdwgFj/vz5ZYdwQGi181Sp+Dmc1KgV3/fNj1JrM5NKkiRJUsEstCRJkiSpYBZakiRJklQwCy1JkiRJKpiFliRJkiQVzEJLkiRJkgpmoSVJkiRJBbPQkiRJkqSCWWhJkiRJUsEstCRJkiSpYBZakiRJklSwWtkBSIVrB94PLMrvbwLWNOxvA84ETs379AMbgDuArpkKUpKkGWZ+lGaUhZYOPucwmkTGqgAXAsc0tNWAU4DjgauAzdMZnCRJJTE/SjPKqYM6uKwEVgMDk+xfzWgSeRD4OPBloA7MAS6Y7gAlSSqB+VGacRZaOnhUyBJBBbhtkj6nN9y+FegF1pFNjQA4Elg+TfFJklQG86NUiqYLrYioRsR9EfEv0xmQ9KKd+f+3d+dRctZ3fu/fv1p609JSawMkQDBarpGwsSLweGDkmQEHvOAhufGMGfueOflDjo3vYOae65yMlRz73vyBfUnsTM4hCTKON2Zsxiw58UQw4GAIjMGM2BdJGLMILahbu1q9VtXv/vGUukvqFl2Snuqn1P1+nVNHTz3PU1UflVr9rW89v+f3AIuAV4Gt42zPV7cDDACHa7bVjj1f3JB0kqYo66OanvVRysSpHNH6Msl3G1Lz6QLWkRSITSfZp52kmEBygm+tgZrlGelGkzTlWR/VvKyPUmbqarRCCEuATwB3NjaOdJquJ5kt6WGg9zQeH9KNI2l6sD6q6VkfpczUe0TrPwD/kuSUyHGFED4fQtgcQtjc0+McoJpEF1Vve4GdwDnAgprtheq6CJSr69pOeI7WmuWjjYkpaUqyPqp5WR+lTE3YaIUQPgl0xxifea/9YowbY4xrY4xrFyxY8F67Suk6VgTmA1+o3j5bs31hdd1yYE/NY2bX7FP7I7uzMTElTS3WRzU966OUqXqOaF0JfCqE8BbwE+APQgh3NTSV1CjP1yxfQzIu/RJgaXXdLqB7kjNJOltZHzV1WB+l1E14weIY418AfwEQQvg94P+OMX6uwbmk+m0Fvn7CujnALdXlXYxe+T5HcvHFC4H3V2/HDAI/a1hKSVOM9VFNz/ooZWrCRkuaUirAXcBVwGqgk+TijW8Cj3L8NLaSJE0X1kcpdafUaMUYHyX57yY1t4OM/RbvmGHgF9WbJKXA+qizhvVRmjSnch0tSZIkSVIdbLQkSZIkKWU2WpIkSZKUMhstSZIkSUqZjZYkSZIkpcxGS5IkSZJSZqMlSZIkSSmz0ZIkSZKklNloSZIkSVLKbLQkSZIkKWU2WpIkSZKUMhstSZIkSUpZIesAai5DQ0NZRxgjxph1hDEuvfTSrCOM68knn8w6whgtLS1ZRxijGTPt27cv6wjHKZVKWUeQNIFmrI8hhKwjjKsZ3ytNfR7RkiRJkqSU2WhJkiRJUspstCRJkiQpZTZakiRJkpQyGy1JkiRJSpmNliRJkiSlzEZLkiRJklJmoyVJkiRJKbPRkiRJkqSU2WhJkiRJUspstCRJkiQpZYWsA2gK6ekhf+uthKefJrzwAmFoCIDSt79N5aabMg43ufrm9tHzvh4GOgcot5aJuUh+ME/7/nYWbFtAx/4OAIZmDPHupe8y0DlAqa1EJV8hP5Sn/WA7816fx6x3ZzU8a/6BByjcfz/5Z54hdHfD8DCVCy6gfN11DN1yC3R1NTzDuPx5mlD+2Wfp+Na3yL/6Krl9+2BoiNjVRekf/SP6/+zPKF1+edYRJWliLcCXgM7q/V3AxprtReBK4NLqPoPAW8CjQM9khZROnY2WUhN27SJ/++1Zx2gKg7MHOXLekePWldpLHFl8hCPnHuHiX1xMx4EOhjqGOLzk8HH7ldvK9J7TS+85vZz/y/Pp3NVJIxU3bqTw858fty6/ZQv5LVso3H8/fU88AZ2NzTAef54mVti2jZYHHzxuXdizh5ZNmyg+9BCHN22itGZNRukkqU7XMNpknSgHfA64sGZdAVgFLAO+D+xuZDjp9Dl0UKmJnZ2Ub76Z0l13UV6/Pus4mWrpbWHx5sWs2LSCS+67hGV/t4z2/e3JxhwcuuAQAIWBAuc+dy7LH1zOJfddwopNK5i5e+bI8xy88OAkhG1haP16+h5/nN6eHvoeeYTK4sVJ1LfeovjDHzY+wzj8eZpY+eKL6f3Lv+TAs8+yb+dODv7yl5QuuwyAUCrRcu+9GSeUpAksAdYCQyfZvpbRJutF4JvA3UAFaAWub3RA6fR5REvpWbqU8m23AZDfujXjMNmasW8GM/bNGLnfdqSNOW/Pob+rH4BQCSPr2460jezX0tdC1xtd9J7bm+wXQ8OzDtx5J8waHaJYufxyhr/4RVr/9b8GIPf66w3PMC5/niZU+tCHKH3oQyP3yytWMPjHf0zh+eeTFQV/xUtqYjmSRikHPAJcN84+l9Us/xzoB7aQDB28GDgPWAh0NzKodHrqqsIhhLeAI0AZKMUY1zYylDSVxBAZnDk4cnQqN5xjzltzxu5HZLhjmP0X7weSZqzrN5NwftSscc4DGxwcWawsWdL4DDpzpRL53/yG1rvvBqAycyaDf/InGYeaHqyR0mm6ElgEvApsZWyjla9uBxgAakfa95A0WgCLsdFSUzqVrzt/P8a4t2FJpClo28e2MTxjeOR+ob/ABb+84LijWABvfOQN+hb0jdzPDec4/1fnM7NnJpMtvPsuxTvuACB2dFC68cZJz6BTM+eDHyT/zjsj9yuLFnHkRz+ivHJlhqmmHWukdCq6gHUkDdQmxv9E2k7SbEEyAUatgZrlGUhNyXO0pElUai/x9lVvM9A58J77VYoVtv/2dnoX9k5SskTYsYP2T3yCXHc3MZdj4I47iB7ROuvk9uxh1mc+Q/6VV7KOIknju55kNsGHgdMpdY0fWS+dsXobrQg8FEJ4JoTw+fF2CCF8PoSwOYSwuafHuTYlgJUPrGTVvatY/uByZu+YDUC5tcyeVXuO2+/ixy5m1T2rWLFpBV2vJ8MFYyHy7vu6LTmgAAAgAElEQVTfnbSsYds22j/6UXKvvUYsFBj8znco33DDpL2+Tt/B555j3+7dHHjySQY/9SkAcvv303HrrRknmzbes0ZaH6UTXFS97QV2AucAC2q2F6rrIsmAXIDjB4IkE2Ecc7QxMaUzVW+jdWWMcQ3wMeBLIYR1J+4QY9wYY1wbY1y7YMGCsc8gTVMhBlp7W1mwZfT/xdDMsdMrBQItfS0semXRyLrBWSeOlWiM3LPP0nHtteR27CB2dDDwk59Q+qM/mpTXVkqKRSrLl9P/538+sir3m99kGGhaec8aaX2UTnCsSZoPfKF6+2zN9oXVdcuBPTWPmV2zT+1/pZ2NiSmdqboarRjjruqf3cD9wBWNDKWzVKUCe/cmt77R843o6xtdP03s/sBuDp97mKH2ISqhwtCMIfauHP37txxtAaD7fd0cPP8gQx3JfsNtw3RfMnpGb0tvS8Oz5h97jPZPfpKwbx+xq4v+n/2M8rXXNvx1J+TP04Q6NmyguGkTuZ07YWiI3Ftv0f4f/+PI9srSpdmFm0askVIDPV+zfA3JeVuXAEur63bhRBhqWhNOhhFCmAHkYoxHqsv/GPh/G55MZ5/t22kZ5+T7woYNsGEDAEODk3OEJmuHzzvMvuX7xt2WK+VY8GryVVzvwl76VvWNux8VWPTyovG3pajlG98gHEkurhz276fj6quP216+6ir6H3ig4TnG8OdpQi2bNtFenbjkRHHGDPq+8pVJTjT9WCOl07AV+PoJ6+YAt1SXdwEbq8s5kosTXwi8v3o7ZhD4WcNSSmesnlkHFwH3hxCO7f/XMcYHG5pKOst1vdHFkXOPMDhzkEpLBSpQ7Csyo2cG8389n9beZNzEnO1zCDEwOGuQcksyEL3QX6BjXwfzfz2f9oPtWf411OQG/vRPaXn4YfJvvEE4eBAKBSqLFzN85ZX033QTld/6rawjTgfWSKmRKsBdwFXAaqCT5OLGbwKPkkzzLjWpEGNM/UnXrl0bN2/enPrzqvGGhk52afbstLQ0fvjcqbr00kuzjjCuJ598MusIYzTjv18zZtq3b/wjoFm5+uqref75553XK2XWR0111S8dmk4jPu9q+gohPFPPNROd3l2SJEmSUmajJUmSJEkps9GSJEmSpJTZaEmSJElSymy0JEmSJCllNlqSJEmSlDIbLUmSJElKmY2WJEmSJKXMRkuSJEmSUmajJUmSJEkps9GSJEmSpJTZaEmSJElSymy0JEmSJCllhawDqLm0tLRkHeGs8Mwzz2QdYVyzZs3KOsIYg4ODWUc4K8ybNy/rCMcpFCwPkk5djDHrCOMKIWQdYYxmfa+UHo9oSZIkSVLKbLQkSZIkKWU2WpIkSZKUMhstSZIkSUqZjZYkSZIkpcxGS5IkSZJSZqMlSZIkSSmz0ZIkSZKklNloSZIkSVLKbLQkSZIkKWWFrANI005PD/lbbyU8/TThhRcIQ0MAlL79bSo33dSwl62cW6F8ZZm4MEIHkAf6IOwK5J/Kk9s5+r1LnB0prytTuaiS7NsLuV/nyD+eJ/SHhmWUJKkptABfAjqr93cBG2u2F4ErgUur+wwCbwGPAj2TFVLNzkZLmmRh1y7yt98+6a8b50fiinj8ylkQV0ZKy0oUflggtztHnBsZ/tPhpME6phMqaytULqpQ/EGRMGCzJUmawq5htMk6UQ74HHBhzboCsApYBnwf2N3IcDpbOHRQmmSxs5PyzTdTuusuyuvXT9rrhgOB/N/mKd5epPjNIsU7ioRd1YYpD5VVFQBKv1caabLyD+Yp3lYk/7/yyYp5UF5XnrTMkiRNuiXAWmDoJNvXMtpkvQh8E7gbqACtwPWNDqizhY2WNNmWLqV8221UPv1pWLRo0l42tyNH/sU84VAglANhXyD3cs2vgKTPIi6tHvUqQe7ZHGE4kPuH0f0qqytETjgyJknSVJAjaZRywCMn2eeymuWfA/3AFpKhgwDnAQsblE9nlboarRDCnBDCPSGErSGELSGEDzc6mKTGiSES50cqq6vd1SDkX6wetSpO8OA2YG4j00lnD+ujNMVcCSwCXgW2jrM9X90OMAAcrtlWe27W4oak01mm3nO0/hJ4MMb4z0IILRx/9oaks8jQTUMwp2bFESjcUyDsTYYRhj2BuDhCASprKuReylG5vHLcc8SOSDjgeVoS1kdp6ugC1pE0UJsY/1NyO0mzBckEGLUGapZnpJ5OZ6EJj2iFEGaT/Nh9FyDGOBRjPNjoYJImySwo/XGJyoKkmco/nh8ZRli+rszwV4bHnpflaVqS9VGaaq4nGdXxMNB7Go/3+0edoJ6hgxeTHAz9XgjhuRDCnSGEMX16COHzIYTNIYTNPT3Oayk1q5b/1ELxG0WK/6VI2FKtCh1Q/kjSPeXeyFG4u0B4J8AwcBRyL+cI3aMVJBy2mkhYH6Wp46LqbS+wEzgHWFCzvVBdFxn9srHthOdorVk+2piYOrvU02gVgDXAf44xfpDkR+dfnbhTjHFjjHFtjHHtggULTtwsqYmESiDsD+T/Pj+yLnaNTnCRezNH8UdFWm5roeUvW8g/nCd2JttDdyD02WhJWB+lqeNYkzQf+EL19tma7Qur65YDe2oeM7tmn9r/3jsbE1Nnl3oarR3Ajhjjr6r37yEpLJJOR6UCe/cmt76+0fV9faPrG6B0TYnK8gpxViTmInFOpPLh0XOvjp1zFWdHyqvKxBmRmI9UFlUo/e+lkSKUe8rJSqUq66M0HT1fs3wNyXlblwBLq+t2Ad2TnElNacLJMGKM74YQ3gkhrIwxbgOuJpmLRdLp2L6dlpUrx6wubNgAGzYAMDR44hm2Z66yokLlisr4G4cg/0RydCvOjJT/sEx5nBOxcs/nyL+cH7Nemo6sj9IUshX4+gnr5gC3VJd3ARuryzmSixNfCLy/ejtmEPhZw1LqLFPvrIN/BvxVdUalN4B/3rhIkhoh/1yeyrJKMkSwjWTCi8OQeztH/lf5kSNaoTcQXgvEc2Iyf1opGS6Yey5H/hWbLOkE1kdpuqkAdwFXAauBTpKLG78JPMrx07xrWqur0YoxPk9yHWxJZ2rp0oYcsZpI/sk8+ScnbpTC4UDxnokupiUJrI/SlHaQsUe5jhkGflG9SSfhyRaSJEmSlDIbLUmSJElKmY2WJEmSJKXMRkuSJEmSUmajJUmSJEkps9GSJEmSpJTZaEmSJElSymy0JEmSJCllNlqSJEmSlDIbLUmSJElKmY2WJEmSJKXMRkuSJEmSUmajJUmSJEkpK2QdYLIMDQ1lHWGMlpaWrCPoNDXrv93g4GDWEcZobW3NOsIYzfg+SZIaJ8aYdYQxrI/1acbP8PXyiJYkSZIkpcxGS5IkSZJSZqMlSZIkSSmz0ZIkSZKklNloSZIkSVLKbLQkSZIkKWU2WpIkSZKUMhstSZIkSUqZjZYkSZIkpcxGS5IkSZJSVsg6QNPr6SF/662Ep58mvPACYWgIgNK3v03lppsyDied/SrnVihfWSYujNAB5IE+CLsC+afy5HaOfh8UZ0fK68pULqok+/ZC7tc58o/nCf0hq7+CJEmpsz6eoSb4DG+jNYGwaxf522/POoY0ZcX5kbgiHr9yFsSVkdKyEoUfFsjtzhHnRob/dDgpIMd0QmVthcpFFYo/KBIGpmkxkSRNOdbHM9MMn+FttCYQOzsp33wz8YorCI89Rv4738k6kjSlhAOB/N/myb2dg15gDpSuLxHPi5CHyqoKud05Sr9XGiki+Qfz5F7KUflQhfK6MsyD8royhYf8lSZJmhqsj2emGT7DT793/VQtXUr5ttsAyG/dmnEYaerJ7cjBjpoV+yD3co7yeeXkfiX5Iy6tfqtXgtyzOQKB3D/kkkICVFZXiA9FAtPvWztJ0tRjfTxDTfAZfsLJMEIIK0MIz9fcDocQbpmMcJKmlxgicX6ksrpaPQYh/2I+WS5O8OA2YG4j00ljWSMlTQbr49lpwiNaMcZtwGUAIYQ8sBO4v8G5JE0zQzcNwZyaFUegcE+BsDf5Bi7sCcTFEQpQWVNJhkZcXjnuOWJHJByYZt/YKVPWSEmNZn08e53q9O5XA7+JMb7diDCSNGIWlP64RGVBUizyj+dHhkmUrysz/JXhkWERI064K00ya6SkxrM+njVOtdH6DPDj8TaEED4fQtgcQtjc09Nz5skkTSst/6mF4jeKFP9LkbCl+q1bB5Q/klSH3Bs5CncXCO8EGAaOJmPVQ/foN3ThsN/WKVPj1kjro6QzYX08e9U9GUYIoQX4FPAX422PMW4ENgKsXbs2jrePJL2XUAmwH/J/n6f0vhIAsWv010nuzRy5N2uuG9IeGb5pOHlsdyD0WUiUjfeqkdZHSWfK+nh2OpVZBz8GPBtj3NOoME2pUoH9+5Plvr7R9X19sHdvsjx//uTnkqaI0jUlcm/nCO8GOArMhsqHR8eWHxtTHmdHKudXyL2Vg4Hk+iLlj5ahNdkv99SpHqCXUjU9a6SkhrE+nqEm+Ax/Ko3WjZxk2OCUtn07LStXjlld2LABNmwAYGhwcLJTSVNGZUWFyhWV8TcOQf6JZFalODNS/sMy5XEGmueez5F/Od/ImNJEpmeNlNQw1scz1ASf4etqtEIIHcBHgX/R0DSSpp38c3kqyyrJEIg2khN6D0Pu7Rz5X+VHvrELvYHwWiCeE5MLM5aS4RC553LkX5mmRURNwRopqRGsj2e/uhqtGGMfMK/BWZrT0qUesZIaKP9knvyTExeCcDhQvGeii4VIk29a10hJDWN9PENN8Bl+mg7alCRJkqTGsdGSJEmSpJTZaEmSJElSymy0JEmSJCllNlqSJEmSlDIbLUmSJElKmY2WJEmSJKXMRkuSJEmSUmajJUmSJEkps9GSJEmSpJTZaEmSJElSymy0JEmSJCllNlqSJEmSlLJCI550YGCA1157rRFPfdpWrFiRdQRpWhocHMw6whgrV67MOsIY27ZtyzqCJGkSNWN9DCFkHWGMGGPWEU6bR7QkSZIkKWU2WpIkSZKUMhstSZIkSUqZjZYkSZIkpcxGS5IkSZJSZqMlSZIkSSmz0ZIkSZKklNloSZIkSVLKbLQkSZIkKWU2WpIkSZKUMhstSZIkSUpZIesA42l96SW6vvMd2p95hvyhQ5RnzWJo2TL2r19P37p1WceTNMUNdA2wb/U+BucMUm4tE3OR/GCetn1tdG3pon1v+5jHxBDZfu12BrsGAcgN5Fh237LJji5J0uRpAb4EdFbv7wI21mwvAlcCl1b3GQTeAh4FeiYrZHaartGa9d/+G+d89auEcnlkXWH/fgpPP03/mjU2WpIabrBzkKNLjh63rtxR5mjHUY4uPsr5D59P+77jm60DKw+MNFmSJE0L1zDaZJ0oB3wOuLBmXQFYBSwDvg/sbmS47DVVo1V8800W/Zt/QyiXGT73XLq/9jX6L7+cMDRE60svQc6RjpIar+VIC4ueWkTHng7y/XmGZw7z7offZXDeIOTgyIVHjmu0hmcMs+/9+wjDgViMGSaXJGmSLAHWAkMkR7ZOtJbRJutF4AFgKfBpoBW4nuOPfk1BdXUuIYQ/DyG8EkJ4OYTw4xBCWyPCzP3Rj8gNDQHw7q23cvT3f5/KzJmUu7ro+8hH6Pvd323Ey0rScdr3ttP5RifFo0VylRyth1uZ/ebske2hEo7bf8/le4iFyPwX5092VGVssuqjJDWVHEmjlAMeOck+l9Us/xzoB7aQDB0EOA9Y2KB8TWLCRiuEsBi4GVgbY1wN5IHPNCJM+5NPAhCLRWY88QQX/cEfsGz1ai782MeYc9ddEP2mWNLkiiEyOHuQwxcdBiA3nGP2G6NN1+Glh+k7r4/Wfa3M2TYnq5jKwGTWR0lqKlcCi4BXga3jbM9XtwMMAIdrttWem7W4IemaRr1DBwtAewhhGOggOdUtdcXdyUDNMDxM1513jqxvfeMNFv7bf0th9272fuUrjXhpSRrjjU+9QWlmaeR+vi/PeY+fR+vhVgDKLWV61vRABc751TkEwsmeSlPXpNRHSWoaXcA6kgZqE+N3E+0kzRYkE2DUGqhZnpF6uqYy4RGtGONO4N8B20lOWTsUY3zoxP1CCJ8PIWwOIWw+cODAaYUJpdEPNEfXreP1p5/m7XvvpdLRAcDc732P/L59p/XcknSmyh1ldn5kJ4NzkqrRs6aHcluZri1dtB5szTidJtvp1MeenmkwzZakqe16ktkEHwZ6T+Px0+g7yXqGDs4F/hC4iGQ05YwQwudO3C/GuDHGuDbGuHbu3LmnFaY8Z3TYzcHPfIZKZyeDq1fT9zu/k2Qpl2ndtu20nluSTtXF//1ilv94OUt/tpSZ22cCUGmrsPf9exmaOcThiw+TG8gxY8cMBuYMMDCn5mu6HAzMGaDUVjrJs+tsdzr1ccGCBZMdU5LSc1H1thfYCZwD1P5aK1TXReDYBOInnrla+73kUaa0eibDuAZ4M8bYE2McBu4DfqcRYQZWr55wn0qb5xlLmjwhBlqOtND1ctfIuuFZw1QKFSBpvN659h22f3w72z++fWSfSkuF7R/fzsHlByc9sybNpNVHSWoKx5qk+cAXqrfP1mxfWF23HNhT85jZNfvUNmY7GxOzWdTTaG0HfjuE0BFCCMDVJHOGpO7wDTeMLHfefTe5Q4doffllOn75SwDKnZ0MrlrViJeWpBHda7rpXdzLcMcwMRcZmjHEgUtGh0QXe4sZplMTmbT6KElnnedrlq8hOW/rEpIp3iE5o7V7kjNNsgknw4gx/iqEcA/wLFACnqNBs973XncdR669lll/93fMfOwxll1xxWiOEOj+6leJrZ4HIamxepf0cvB/G/9IVBgOzHtpHm0H21jx1yvGbH/tT14DIDeQY9l9yxqaU9mazPooSU1hK/D1E9bNAW6pLu9i9LdgjuTixBcC76/ejhkEftawlE2jrlkHY4xfA77W4CwA7P73/56BSy9l9v33U9y+ndjaysAHPsD+9evp//CHJyOCpGluzutz6D2vl+FZw5RbyoQYKBwt0NHdwdytc2k5Mt6VGTUdTWZ9lKSzSgW4C7gKWA10klzc+E3gUY6f5n2Kqnd698lTLHJg/XoOrF+fdRJJ01TXq110vdo18Y7jGO8olyRJU9ZBxh7lOmYY+EX1Ng3Vc46WJEmSJOkU2GhJkiRJUspstCRJkiQpZTZakiRJkpQyGy1JkiRJSpmNliRJkiSlzEZLkiRJklJmoyVJkiRJKbPRkiRJkqSU2WhJkiRJUspstCRJkiQpZTZakiRJkpSyQiOetK2tjRUrVjTiqaWm0Nvbm3WEcc2cOTPrCGM043v10ksvZR1hjKVLl2Yd4Ti7d+/OOoIkaZLFGLOOMEYIIesIp80jWpIkSZKUMhstSZIkSUqZjZYkSZIkpcxGS5IkSZJSZqMlSZIkSSmz0ZIkSZKklNloSZIkSVLKbLQkSZIkKWU2WpIkSZKUMhstSZIkSUqZjZYkSZIkpayQdQBpOsk/8ACF++8n/8wzhO5uGB6mcsEFlK+7jqFbboGurqwjNoWmfZ96esjfeivh6acJL7xAGBoCoPTtb1O56aaGvvTg/EEOfeAQQ3OHqLRViLlIfjBPS08LnS910trTCsDAOQPs+diekz7PwocW0r6zvaFZJUnKTAvwJaCzen8XsLFmexG4Eri0us8g8BbwKNCTbhQbLWkSFTdupPDznx+3Lr9lC/ktWyjcfz99TzwBnZ0nefT00azvU9i1i/ztt0/66wIMzxmm/4L+49aVO8r0X9hP//n9nPM/zqF1b2sm2SRJahrXMNpknSgHfA64sGZdAVgFLAO+D+xOL4pDB6XJ1NLC0Pr19D3+OL09PfQ98giVxYsByL31FsUf/jDjgE2iSd+n2NlJ+eabKd11F+X16yf1tQuHC8x7Yh6Lf7qYC35wAefddx4tPS3JxhwcvfjomMcs/uliLvzehcfdPJolSZqylgBrgaGTbF/LaJP1IvBN4G6gArQC16cbp65GK4Tw5RDCyyGEV0IIt6QbQZo+Bu68k6FvfYvKZZdBWxuVyy9n+ItfHNmee/31DNM1j6Z9n5YupXzbbVQ+/WlYtGhSX7qtu42Zv55JobdAqASKh4rM+M2Mke2hEiY1jxLWR0lqEjmSRikHPHKSfS6rWf450A9sIRk6CHAesDC9SBMOHQwhrAbWA1eQ9IcPhhD+R4zx1+nFkKaJWbPGrhscHFmsLFkyiWGamO/Te4ohMjx7mKO/lRzFCkOBGb+eMWa/3Z/cTaW1Qm4oR2t3K7Nfmk1bd9tkx52yrI+S1ESuBBYBrwJbgetO2J6vbgcYAA7XbOsBLq4uLwa604lUzzla7wOeijH2AYQQHgP+CfD/pRNBmr7Cu+9SvOMOAGJHB6Ubb8w4UXPyfRq145/toDyrPHI/35dnwf9cQMuhljH7VtoryZ9tFfov6Kd/ST8LHllAxzsdk5Z3irM+SlIz6ALWkTRQmxi/w2knabYgmQCj1kDN8tjvLU9bPUMHXwbWhRDmhRA6gI8D55+4Uwjh8yGEzSGEzT09KU/ZIU1BYccO2j/xCXLd3cRcjoE77iBO8yM14/F9em/ljjLdH+1maG4yID3fn2fu03M5975zOf+H57P4bxaPHu3KwYErDmSYdsqxPkpSM7ieZDbBh4He03h8g0bfT3hEK8a4JYTwTUajvwCUxtlvI9XJE9euXRtTzilNKWHbNtpvuIHcjh3EQoHBO+6gfMMNWcdqOr5PYy25ZwkxREqzShxcc5C+i/qotFU4uOYgC//nQoqHihQPFUf2zx3NMe/v59F3UR+xECnNLlFuLZMfzL/Hq6ge1kdJagIXVW97gZ3AOUDtGQiF6rojQJnkqNaJo+hrJ+0dO7fUaatrMowY43djjGtijOuA/YDjz6XTlHv2WTquvTZpHjo6GPjJTyj90R9lHavp+D6dXIiB4uEinS+Ozl87PHsYgMhJPsf78b4hrI+SlLFjTdJ84AvV22drti+srlsO7Kl5zOyafRbULO9ML1pd19EKISyMMXaHEC4A/inw4fQiSNNH/rHHaLvxRsKRI8SuLvp/+lMqV1yRdaym07TvU6UC+/cny319o+v7+mDv3mR5/vyGvPT+K/bT9m4bLftayPfnKXWUOHTpoZHtxSPJUax9V+0j35dnxpszKB4qUm4rc3DNQWIx6bSKB4sezUqR9VGSziLPk8wsCMn1th4gORq2tLpuF6lNhAH1X7D43hDCPGAY+FKM0UH+0mlo+cY3CEeOABD276fj6quP216+6ir6H3ggi2hNpWnfp+3baVm5cszqwoYNsGEDAEODJ55hm46+C/o4surIuNvCcKDz+eToVqWlwtHlRzn8gcNjdyzB3KfmNiTfNGZ9lKQsbQW+fsK6OcCxC27sojp4m2Qs3yqSa2m9v3o7ZhD4WbrR6mq0Yoy/m+7LSpJOxaxts+g/v5/h2cNUWiqEGMj35ml7t43Zr8ymeDg5ojX7ldnkB/IMLhyk3FGmUqiQH8jTtruN2S/NpuXg2NkJdfqsj5J0FqkAdwFXAauBTpKLc7wJPEoyzXuK6j2iJSkFHq2qT9O+T0uXNuyI1UQ6X+qk86XOCfdr29NG2x6vlSVJmsYOMvYo1zHDwC+qtwarazIMSZIkSVL9bLQkSZIkKWU2WpIkSZKUMhstSZIkSUqZjZYkSZIkpcxGS5IkSZJSZqMlSZIkSSmz0ZIkSZKklNloSZIkSVLKbLQkSZIkKWU2WpIkSZKUMhstSZIkSUpZiDGm/6Qh9ABvp/BU84G9KTxPmsxUHzPVx0z1a8ZcUznThTHGBSk8j2qkWB9hav/8pclM9TFTfcxUn6meqa4a2ZBGKy0hhM0xxrVZ56hlpvqYqT5mql8z5jKTstSM/9Zmqo+Z6mOm+pipPllkcuigJEmSJKXMRkuSJEmSUtbsjdbGrAOMw0z1MVN9zFS/ZsxlJmWpGf+tzVQfM9XHTPUxU30mPVNTn6MlSZIkSWejZj+iJUmSJElnHRstSZIkSUpZ0zRaIYTrQgjbQgivhxD+VdZ5AEII/zWE0B1CeDnrLMeEEM4PIfwihLAlhPBKCOHLTZCpLYTwdAjhhWqm/yfrTMeEEPIhhOdCCH+bdRaAEMJbIYSXQgjPhxA2Z50HIIQwJ4RwTwhha/Xn6sMZ51lZfX+O3Q6HEG7JMlM1159Xf75fDiH8OITQ1gSZvlzN80ozvEdqnGarkdbHujNZH+tkfaw7kzWyvjxNUR+b4hytEEIeeA34KLAD+AfgxhjjqxnnWgf0Aj+MMa7OMssxIYRzgXNjjM+GEGYBzwA3ZPlehRACMCPG2BtCKAJPAF+OMT6VVaZjQgj/F7AWmB1j/GQT5HkLWBtjbJqL+IUQfgA8HmO8M4TQAnTEGA9mnQtGfjfsBD4UY0zrIq+nk2Mxyc/1JTHG/hDC3wCbYozfzzDTauAnwBXAEPAg8MUY46+zyqTGaMYaaX2sO5P1sf48b2F9PCXWyJPmaZr62CxHtK4AXo8xvhFjHCJ5c/4w40zEGP8XsD/rHLVijLtjjM9Wl48AW4DFGWeKMcbe6t1i9ZZ5Bx9CWAJ8Argz6yzNKoQwG1gHfBcgxjjUTEUEuBr4TZYFpEYBaA8hFIAOYFfGed4HPBVj7IsxloDHgH+ScSY1RtPVSOtj3Zmsj2eps6A+gjXyZJqmPjZLo7UYeKfm/g4y/uV4NgghLAU+CPwq2yQjQxCeB7qBh2OMmWcC/gPwL4FK1kFqROChEMIzIYTPZx0GuBjoAb5XHUJyZwhhRtahanwG+HHWIWKMO4F/B2wHdgOHYowPZZuKl4F1IYR5IYQO4OPA+RlnUmNYI0+R9XFC1seJNXt9BGvkyTRNfWyWRiuMsy7zb3yaWQhhJnAvcEuM8XDWeWKM5RjjZcAS4IrqYdvMhBA+CXTHGJ/JMsc4rowxrgE+BnypOvwmSwVgDauBLvIAAAJgSURBVPCfY4wfBI4CmZ//AVAdpvEp4KdNkGUuyRGEi4DzgBkhhM9lmSnGuAX4JvAwybCIF4BSlpnUMNbIU2B9fG/Wx7o1bX0Ea+R7aab62CyN1g6O7zSXkP2wnKZVHed9L/BXMcb7ss5Tq3pY/VHguoyjXAl8qjrm+yfAH4QQ7so2EsQYd1X/7AbuJxkSlKUdwI6ab1jvISkszeBjwLMxxj1ZBwGuAd6MMfbEGIeB+4DfyTgTMcbvxhjXxBjXkQzj8vysqckaWSfrY12sj/Vp5voI1sj31Cz1sVkarX8AlocQLqp26J8B/nvGmZpS9cTa7wJbYozfyjoPQAhhQQhhTnW5neQ/3NYsM8UY/yLGuCTGuJTk5+mRGGOmRyBCCDOqJ2hTHX7wj0kOb2cmxvgu8E4IYWV11dVAppPQ1LiRJhgSUbUd+O0QQkf1/+DVJOd/ZCqEsLD65wXAP6V53i+lyxpZB+tjfayP9Wny+gjWyPfULPWxkMWLnijGWAoh/J/A3wF54L/GGF/JOBYhhB8DvwfMDyHsAL4WY/xutqm4Evg/gJeqY74Bvhpj3JRhpnOBH1Rnv8kBfxNjbIrpYpvMIuD+5HcQBeCvY4wPZhsJgD8D/qr6Ae4N4J9nnIfqmOqPAv8i6ywAMcZfhRDuAZ4lGX7wHLAx21QA3BtCmAcMA1+KMR7IOpDS14w10vpYN+tjfayPp8AaWZemqI9NMb27JEmSJE0lzTJ0UJIkSZKmDBstSZIkSUqZjZYkSZIkpcxGS5IkSZJSZqMlSZIkSSmz0ZIkSZKklNloSZIkSVLK/n+VpKkJcooOjgAAAABJRU5ErkJggg==\n", 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" ] @@ -526,20 +1080,191 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "ename": "KeyError", + "evalue": "'x'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m df[df['cluster'] == 0].plot(ax=ax, kind='scatter', x='x', y='y',\n\u001b[0;32m----> 5\u001b[0;31m label=0, color='red')\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/hlavaj20/miniconda3/lib/python2.7/site-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m 2939\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2940\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2941\u001b[0;31m sort_columns=sort_columns, **kwds)\n\u001b[0m\u001b[1;32m 2942\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_frame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2943\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/hlavaj20/miniconda3/lib/python2.7/site-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36mplot_frame\u001b[0;34m(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m 1975\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1976\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1977\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/hlavaj20/miniconda3/lib/python2.7/site-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1741\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1742\u001b[0m plot_obj = klass(data, x=x, y=y, subplots=subplots, ax=ax,\n\u001b[0;32m-> 1743\u001b[0;31m kind=kind, **kwds)\n\u001b[0m\u001b[1;32m 1744\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1745\u001b[0m raise ValueError(\"plot kind %r can only be used for data frames\"\n", + "\u001b[0;32m/home/hlavaj20/miniconda3/lib/python2.7/site-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, x, y, s, c, **kwargs)\u001b[0m\n\u001b[1;32m 843\u001b[0m \u001b[0;31m# the handling of this argument later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 844\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mScatterPlot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 846\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 847\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/home/hlavaj20/miniconda3/lib/python2.7/site-packages/pandas/plotting/_core.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, x, y, **kwargs)\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 818\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m \u001b[0;32mif\u001b[0m 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"\u001b[0;31mKeyError\u001b[0m: 'x'" + ] + }, { "data": { + "image/png": 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"text/plain": [ - "array([[150, 0],\n", - " [ 5, 145]])" + "
" ] }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "matrix_full" + "fix, ax = plt.subplots()\n", + "\n", + "i = 0\n", + "df[df['cluster'] == 0].plot(ax=ax, kind='scatter', x='x', y='y',\n", + " label=0, color='red')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# this is the argmin calculation for delta_medoids_full algorithm\n", + "def find_best_cluster_representative(cluster, similarity_measure):\n", + " #input: np.array(of points)\n", + " #returns a tuple\n", + " min_sum = float(\"inf\")\n", + " best_repr_index = None\n", + " for i in range(len(cluster)):\n", + " distance_sum = 0;\n", + " for point in cluster:\n", + " distance_sum += similarity_measure(cluster[i], point)\n", + " if (distance_sum > min_sum):\n", + " break\n", + " if(distance_sum < min_sum):\n", + " min_sum = distance_sum\n", + " best_repr_index = i\n", + "\n", + " return cluster[best_repr_index]\n", + "\n", + "def delta_medoids_full(df, delta, similarity_measure):\n", + "\n", + " t = 0 #iteration number\n", + " representatives = {} #selected representatives\n", + " representatives[t] = set() #representatives for given iteration\n", + " size_threshold = 0.03 * len(df.index)\n", + "\n", + " while True:\n", + " #print(\"\\n=========== running for t = \" + str(t) + \"============\")\n", + " clusters = {} #subclusters inside cluster\n", + " for item in representatives[t]:\n", + " clusters[item] = np.array(item, ndmin=2)\n", + "\n", + " print 'clusters before rep assign', len(clusters.keys())\n", + " t = t + 1\n", + "\n", + " #================== RepAssign starts ==================\n", + " for row in df.iterrows():\n", + " dist = float(\"inf\")\n", + " represenative = None\n", + "\n", + " point = tuple(row[1])\n", + "\n", + " for rep in clusters.keys():\n", + " #finding the closest representative to current point\n", + " if similarity_measure(point, rep) <= dist:\n", + " representative = rep\n", + " dist = similarity_measure(point, rep)\n", + " if dist <= delta:\n", + " clusters[representative] = np.vstack((clusters[representative], point))\n", + " elif t == 1:\n", + " clusters[point] = np.array(point, ndmin=2)\n", + " #================== RepAssign ends ===================\n", + "\n", + " \n", + " if (len(clusters.keys()) > 30) or (len(clusters.keys()) > len(df.index) * 0.05):\n", + " for key in list(clusters.keys()):\n", + " if len(clusters[key]) <= size_threshold:\n", + " tmp_sim = 0\n", + " max_sim = 0\n", + " new_key = key\n", + " for key_sim in list(clusters.keys()):\n", + " tmp_sim = similarity_measure(key, key_sim)\n", + " if key_sim == key:\n", + " continue\n", + " elif tmp_sim > max_sim:\n", + " new_key = key_sim\n", + " max_sim = tmp_sim\n", + " \n", + " if new_key == key:\n", + " print('Dropping this cluster, it is not relevant.')\n", + " else:\n", + " clusters[new_key] = np.vstack((clusters[new_key], clusters[key]))\n", + " del(clusters[key])\n", + " print 'clusters in loop', len(clusters.keys())\n", + " \n", + " representatives[t] = set()\n", + " print 'clusters out of loop', len(clusters.keys())\n", + " for cluster in clusters.values():\n", + " representative = find_best_cluster_representative(cluster, similarity_measure)\n", + " #print(representative)\n", + " representatives[t].add(tuple(representative))\n", + " #print(representatives[t])\n", + "\n", + " if representatives[t] == representatives[t-1]:\n", + " break\n", + "\n", + " #print(\"delta_medoids_full algorithm ended after \" + str(t) + \" iterations.\")\n", + " return pd.DataFrame(list(representatives[t]), columns=df.columns.values)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#delta medoids full\n", + "dft = df[df['cluster'] == 0]\n", + "dta = butils.estimate_delta(dft, distance.euclidean)\n", + "medoids_full_result = delta_medoids_full(dft, dta, distance.euclidean)\n", + "one_shot_medoids_result = butils.delta_medoids_one_shot(dft, dta, distance.euclidean)\n", + "# test_res[\"delta_medoids_full\"][name] = medoids_full_result\n", + "# train_delta_medoids_full = train_delta_medoids_full.append(medoids_full_result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fix, ax = plt.subplots()\n", + "\n", + "i = 0\n", + "dft.plot(ax=ax, kind='scatter', x='x', y='y',label=0, color='red', s=50)\n", + "medoids_full_result.plot(ax=ax, kind='scatter', x='x', y='y',label='medoids', color='blue', figsize=(20,20), s=150)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fix, ax = plt.subplots()\n", + "\n", + "i = 0\n", + "dft.plot(ax=ax, kind='scatter', x='x', y='y',label=0, color='red', s=50)\n", + "one_shot_medoids_result.plot(ax=ax, kind='scatter', x='x', y='y',label='medoids', color='blue', figsize=(20,20), s=150)\n", + "plt.show()" ] }, { diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 9365145..2a49cbb 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -85,16 +85,12 @@ \chapter{Introduction} Multi-layer defense systems are protecting contemporary networks. As attackers overcome one layer, there is yet another layer waiting for them. -However, if the attackers succeed to bypass all -%VH of them -{\cbl security layers} -unnoticed, a huge problem can occur. -%VH They -{\cbl Attackers} can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. +However, if the attackers succeed to bypass all of them unnoticed, a huge problem can occur. + +Attackers can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. It can take a long time before somebody notices the breach. A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. -%VH \\ One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). In NBAD, devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. @@ -107,23 +103,19 @@ \chapter{Introduction} In order to detect such anomalies, it is needed to focus on smaller parts of the network. Behavioral clustering of network hosts can be used to separate it into smaller pieces. \\ -In Cognitive targeted anomaly detection framework from Cisco, -%VH comma added -behavioral clustering is used to find groups of similar hosts in a network. +In Cognitive targeted anomaly detection framework from Cisco, behavioral clustering is used to find groups of similar hosts in a network. Each cluster created consists of hosts that have similar behavior. The main focus of this work is to find a representation of these clusters that are used to calculate the model behavior. -%VH -{\cbl The representation used in this thesis is based on cluster prototypes, which will be described in the sequel.} -These cluster prototypes\todo{Can I use the term cluster prototype before first explaining it? VH: I think I solved it.} are then updated dynamically as the behavior of the network changes. \\ +The representation used in this thesis is based on cluster prototypes, which will be described in Chapter~\ref{ch:theory} +These cluster prototypes are then updated dynamically as the behavior of the network changes. \\ Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. \\ The thesis is organized as follows. -{\cbl -Chapter~\ref{ch:ids} {\cbl introduces the} anomaly-based network security. +Chapter~\ref{ch:ids} introduces the anomaly-based network security. Chapter~\ref{ch:theory} explains the theory behind clustering and representative selection in metric and non-metric spaces. -Chapter~\ref{ch:datasets} presents datasets used for testing.} +Chapter~\ref{ch:datasets} presents datasets used for testing. Chapter~\ref{ch:experiments} covers all the experiments with their results and is followed by the conclusion of this thesis. %The main focus of this work is in representing these clusters found by clustering algorithms. @@ -151,39 +143,25 @@ \chapter{Introduction} \chapter{Goals} -This work aims to study methods used for the representation of network host behavioral clusters that can be -%VH used; not to repeat the same word -{\cbl applied} in Cognitive targeted anomaly detection framework. -Behavioral similarity used for clustering in this framework does not form a metric space, which creates a requirement for algorithms used for representative selection. {\cbl Typesetting. See \%VH comment.} -%VH \\ This is not a good typesetting approach. Use \smallskip or \medskip or \bigskip -%VH Even the solution above is not the best one. Changing the style would be the way. -%VH My advice: Follow the style and do not put extra space after paragraphs. +This work aims to study methods used for the representation of network host behavioral clusters that can be applied in Cognitive targeted anomaly detection framework. +Behavioral similarity used for clustering in this framework does not form a metric space, which creates a requirement for algorithms used for representative selection. \medskip This thesis should present a method that can be used for finding the cluster prototypes of clusters in non-metric space. These cluster prototypes represent the whole cluster in a way that is convenient for further computation. -It can be, for example, assigning newly added samples to their corresponding cluster. -%VH\\ +It can be, for example, assigning newly added samples to their corresponding cluster.\\ \medskip Networks monitored by Cognitive targeted anomaly detection framework can have up to several hundreds of thousands of hosts which leads to massive clusters. If the classification of newly added hosts were done by comparing the new host to all others, the memory and time needed for classification would be unmanageable. -That is why the number of representatives selected from each cluster should be kept as small as possible. -%VH \\ +That is why the number of representatives selected from each cluster should be kept as small as possible. \\ \medskip General methods used for general cluster representation in non-metric spaces should be studied. From these previously studied methods, the best ones will be selected and implemented. -As part of this thesis, -%VH there should also be created a dataset as a benchmark for testing the chosen method. -%VH Word order corrected -{\cbl a benchmark dataset for testing the chosen method should also be created.} -A further goal is to test the chosen methods -%VH on generally used clustering datasets and the created benchmark dataset. -{\cbl on clustering datasets used by others and on a newly created benchmark dataset.} -Based on this testing, -%VH find -{\cbl the method should be found} that suits best for use in this field and incorporates it into the Cognitive targeted anomaly detection framework. +As part of this thesis, a benchmark dataset for testing the chosen method should also be created. +A further goal is to test the chosen methods on clustering datasets used by others and on a newly created benchmark dataset. +Based on this testing, the method should be found that suits best for use in this field and incorporates it into the Cognitive targeted anomaly detection framework. The selected method should be finetuned to get the best possible results in anomaly-detection. \setsecnumdepth{all} From 404e754f8d6ffb358d2625daeed419a471c77eb3 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 10 May 2019 09:30:52 +0200 Subject: [PATCH 43/65] added centroids explanation --- text/hlavac-thesis.tex | 23 +++++++++++++++-------- text/img/centroids.png | Bin 0 -> 23215 bytes 2 files changed, 15 insertions(+), 8 deletions(-) create mode 100644 text/img/centroids.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 2a49cbb..1394393 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -320,7 +320,6 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} %========================================CHAPTER================================== \chapter{Theoretical Overview}\label{ch:theory} - This chapter introduces a theoretical background to explain clustering methods, the topic of non-metric spaces and the problem of representative selection. \section{Introduction to Clustering}\label{sec:clust_intro} @@ -347,7 +346,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} \begin{equation} \mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:, \end{equation} - where $C$ stands for all clusters the data was clustered into. +where $C$ stands for all clusters the data was clustered into. For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. Given this definition, the best way to cluster every dataset would be to put each object to its cluster. That would not lead to any reasonable result. @@ -405,7 +404,7 @@ \section{Clustering on Topological Space}\label{sec:cluster_topo} \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \mathrm{for} x, y, z \in X$. \end{enumerate} -The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctdaf} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. +The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctadf} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. \\ Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: @@ -447,7 +446,7 @@ \section{Representative Selection} If it was possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. \\ A cluster prototype is a data sample that represents all samples in the data cluster. -According to \cite{tan2014introduction}, there are three motivations for finding the most representative cluster prototypes: +According to \ref{tan2014introduction}, there are three motivations for finding the most representative cluster prototypes: \begin{itemize} \item Summarization \item Compression @@ -486,12 +485,20 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ This definition of representative selection problem serves well for this paper. \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_metric} -\todo{Centroid by si mel zavest a vysvetlit vyhody a nevyhody. Centroid muze byt vazeny nebo aritmeticky prumer, to dokazes zapsat vzorcem a bude to hezky vypadat nebo obrazek nebo tak neco. Medoid si vubec nevysvetlil co je.} + A metric dataset has to have the same number of features for each sample and a metric that fulfills all the requisites in the definition of metric space. Selecting a prototype from this dataset is often best achieved by calculating a centroid for a given cluster. -A centroid can be, for example, the average of all points in the cluster. -If such a centroid is meaningful (it does not fall away from the data), it can be selected as a prototype. -However, if calculated centroid is not meaningful (it falls outside of the dataset), a medoid can be selected as a prototype. +A centroid can be calculated as the mean of the points in the cluster. +An example of a centroid in a cluster can be found in Figure~\ref{img:centroids} a). +Other ways of calculating medoids can be used, e.g. weighted average of all points. \\ + +\begin{figure} + \includegraphics[width=\linewidth]{img/centroids.png} + \label{img:centroids} + \caption{Centroid of a cluster calculated as mean of all points.} +\end{figure} + +However, if a calculated centroid is not meaningful (see Figure~\ref{img:centroids} b)), a medoid can be selected as a prototype. 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zqXNx5c}DUn<95{RRE?N4!n*0pL?p`V>5M^v&Oxn$*YGSSk#h*rwnF=z6a|wu*Is|L zzVyMQU{H`F1u~kNt$l< Date: Fri, 10 May 2019 11:23:17 +0200 Subject: [PATCH 44/65] added medoids img and definition --- text/hlavac-thesis.tex | 82 +++++++++++++++++++++++------------------ text/img/centroids.png | Bin 23215 -> 23780 bytes text/img/medoids.png | Bin 0 -> 23938 bytes 3 files changed, 46 insertions(+), 36 deletions(-) create mode 100644 text/img/medoids.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 1394393..5953432 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -320,13 +320,14 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} %========================================CHAPTER================================== \chapter{Theoretical Overview}\label{ch:theory} + This chapter introduces a theoretical background to explain clustering methods, the topic of non-metric spaces and the problem of representative selection. \section{Introduction to Clustering}\label{sec:clust_intro} As defined in \cite{guttag2016introduction}, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. Without any prior knowledge of the data, this method looks for structures in feature vectors. Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. -Variability and dissimilarity are two properties defined for a better understanding of the data. \\ +Variability and dissimilarity are two properties defined for a better understanding of the data. Variability is defined as \begin{equation} \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:, @@ -351,7 +352,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Given this definition, the best way to cluster every dataset would be to put each object to its cluster. That would not lead to any reasonable result. Therefore there is a constraint added for clustering methods. -It can be either the maximum number of clusters or the maximum distance between two clusters. \\ +It can be either the maximum number of clusters or the maximum distance between two clusters. A straightforward example of clustering is a method called agglomerative hierarchical clustering. Given $N$ objects in a dataset, it creates $N$ clusters - meaning there is a cluster for each object. @@ -359,7 +360,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. This method is a greedy algorithm, and therefore it might not result in globally optimal clustering. Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$. -Therefore, it cannot be used in big datasets. \\ +Therefore, it cannot be used in big datasets. An example of a much faster clustering algorithm that is also greedy is $K$-means. The '$K$' in $K$-means stands for the number of clusters that we want to get as a result. @@ -386,7 +387,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} This algorithm is fast, it has time complexity $\mathcal{O}(k \cdot n)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. It is the most common clustering algorithm as it typically converges in a few iterations. -For more details about basic clustering algorithms see \cite{guttag2016introduction}.\\ +For more details about basic clustering algorithms see \cite{guttag2016introduction}. %summary The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. @@ -394,7 +395,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Next section delves deeper into what it means when a measure does not form a metric space. \section{Clustering on Topological Space}\label{sec:cluster_topo} -This section explains the difference between metric and topological space and clustering approaches for topological spaces. \\ +This section explains the difference between metric and topological space and clustering approaches for topological spaces. A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X \to \mathbb{R}$\todo{ad pripominka, ze by to melo byt NxN, takhle to je popsane v te citovane knize. Prijde mi, ze je to obecnejsi a drzim se toho jak to popsal ten matematik.} which satisfies the following conditions: \begin{enumerate} @@ -405,7 +406,7 @@ \section{Clustering on Topological Space}\label{sec:cluster_topo} \end{enumerate} The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctadf} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. -This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. \\ +This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: \begin{enumerate} @@ -414,22 +415,22 @@ \section{Clustering on Topological Space}\label{sec:cluster_topo} \item [(iii)] The intersection of any two sets of $F$ is in $F$. \item [(iv)] The union of any number of sets of $F$ is in $F$. \end{enumerate} -The ordered pair ($S, F$) is called a topological space. \\ +The ordered pair ($S, F$) is called a topological space. -\todo{explain about neighborhoods} +\todo{explain about neighborhoods??} K-Means clustering from the previous section cannot be used in topological space as the centroids do not exist. In many cases, there is no prior knowledge of k, as is the case in this thesis. -A popular method that is used for clustering in non-metric spaces is called community-based clustering. \\ +A popular method that is used for clustering in non-metric spaces is called community-based clustering. Community-based clustering detects communities in the data. A community is a subset of examples in the data, that is densely connected with each other. One of the ways how to detect communities in a graph is to create a full-adjacency matrix. This matrix contains all connections between all nodes in the given graph. -Analyzing the matrix then can tell us about the densities in different parts of graphs.\\ +Analyzing the matrix then can tell us about the densities in different parts of graphs. Louvain method is a similar approach to clustering when we do not have a simple graph but a set of samples and a pairwise similarity measure. -This method relies on creating a full similarity matrix for the whole dataset and then looking for communities in the data. \\ +This method relies on creating a full similarity matrix for the whole dataset and then looking for communities in the data. In Cognitive Targeted Anomaly Detection Framework Louvain method cannot be used directly as calculating the full similarity matrix has a complexity of $\mathcal(n^2)$, which is impossible to calculate for a large network. That is why an approximative clustering method is used. @@ -443,7 +444,7 @@ \section{Representative Selection} This section focuses on the idea of finding a representation of clusters. Clustering huge datasets can result in big clusters of several tens of thousands of objects in them. Computational operations such as assigning new objects to clusters (e. g. $K$-Nearest Neighbors) are dependent on the number of objects in each cluster or the representation of these clusters. -If it was possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. \\ +If it was possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. A cluster prototype is a data sample that represents all samples in the data cluster. According to \ref{tan2014introduction}, there are three motivations for finding the most representative cluster prototypes: @@ -454,7 +455,7 @@ \section{Representative Selection} \end{itemize} All of these apply to the NBAD aswell. Therefore, summarizing the behavior of the whole cluster into a cluster prototype is desirable. -As is finding the smallest possible number of prototypes for each cluster for efficient finding nearest neighbors when adding new hosts to their corresponding clusters. \\ +As is finding the smallest possible number of prototypes for each cluster for efficient finding nearest neighbors when adding new hosts to their corresponding clusters. \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{sec:def_rep_selection} Representative selection aims to find a minimal subset of examples from a cluster, that carries sufficient information about the whole cluster. @@ -485,7 +486,6 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ This definition of representative selection problem serves well for this paper. \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_metric} - A metric dataset has to have the same number of features for each sample and a metric that fulfills all the requisites in the definition of metric space. Selecting a prototype from this dataset is often best achieved by calculating a centroid for a given cluster. A centroid can be calculated as the mean of the points in the cluster. @@ -493,24 +493,34 @@ \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_met Other ways of calculating medoids can be used, e.g. weighted average of all points. \\ \begin{figure} - \includegraphics[width=\linewidth]{img/centroids.png} + \includegraphics[width=\linewidth]{img/centroids.png} + \caption{Centroids of a cluster calculated as mean of all points.} \label{img:centroids} - \caption{Centroid of a cluster calculated as mean of all points.} \end{figure} -However, if a calculated centroid is not meaningful (see Figure~\ref{img:centroids} b)), a medoid can be selected as a prototype. -A medoid is the most representative point of a cluster. +However, if a calculated centroid is not meaningful (see Figure~\ref{img:centroids} b)\todo{references not working? why?}), a medoid can be selected as an alternative to it. +A medoid is a point that is in the set that minimizes the average distance to all the other points in the set. +It can be thought of as a median of the dataset \todo{this is good for an explanation, but not very scientific, as it is not entirely true, how to explain better?}. +Formally, medoid is defined as: +\begin{equation} +x_{medoid} = \mathrm{argmin}_{y in \{x_1, x_2, ..., x_n\}} \sum_{i=1}^{n}{d(y, x_i)}\:, +\end{equation} +where $x_1, x_2, ..., x_n$ is a set of $n$ points in a space with a distance function $d$. \todo{definition taken from wikipedia - similar is in xu2011network but not that nice - how to cite?? } +In Figure~\ref{img:medoids} medoids were chosen instead of centroids. +\begin{figure} + \includegraphics[width=\linewidth]{img/medoids.png} + \caption{Choice of medoids} + \label{img:medoids} +\end{figure} \subsection{Representative Selection in a Topological Space}\label{sec:rep_select_arbitrary} Clearly, the concept of centroid explained in the previous section cannot be used to solve the problem of finding a representative in an arbitrary space (i.e., topological space). -Medoids, of course, can be selected for datasets in arbitrary spaces. \\ - -$K$-Medoids algorithm that uses the concept of medoids instead of centroids that are used in the $K$-Means algorithm. - +Medoids, of course, can be selected for datasets in arbitrary spaces. -\todo{general approaches} +$K$-Medoids algorithm that uses the concept of medoids instead of centroids that are used in the $K$-Means algorithm while retaining its good properties. +$K$-Medoids is most commonly used for representative selection in topological spaces. -There are not many well-known approaches to solve this problem. +There are not many more well-explored methods for solving this problem. The one that is used in this thesis is explained in \cite{liebman2015representative}. They took the main ideas of $K$-Means clustering and transformed them for usage in a non-metric space with a pair-wise similarity measure. Instead of stating the $K$ in advance they state a parameter $ 0 \leq \delta \leq 1$ that serves as a constraint. @@ -663,8 +673,8 @@ \section{Datasets Created for this Work} They can be easily visualized on graphs and nicely show some of the specific properties of implemented algorithms. \begin{figure} \includegraphics[width=\linewidth]{img/datasets.png} - \label{img:datasets} \caption{Graphs of all datasets} + \label{img:datasets} \end{figure} Similarity measure used in these datasets was Euclidean distance. \\ @@ -745,8 +755,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp1_blobs.png} - \label{img:exp1_blobs} \caption{Confusion matrices for Blobs 3D dataset} + \label{img:exp1_blobs} \end{figure} \textbf{Overlap} @@ -764,8 +774,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp1_overlap.png} - \label{img:exp1_overlap} \caption{Confusion matrices for Overlap dataset} + \label{img:exp1_overlap} \end{figure} \textbf{Moons} @@ -782,8 +792,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp1_moons.png} - \label{img:exp1_moons} \caption{Confusion matrices for Moons dataset} + \label{img:exp1_moons} \end{figure} \textbf{Circles} @@ -800,8 +810,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp1_circles.png} - \label{img:exp1_circles} \caption{Confusion matrices for Circles dataset} + \label{img:exp1_circles} \end{figure} @@ -813,8 +823,8 @@ \subsection{Conclusion} As can be seen on Figure~\ref{img:blobs_border_select}, the algorithm picked the samples on the edge of the dataset that did not cover many points in their neighborhoods. \begin{figure} \includegraphics[width=\linewidth]{img/delta_medoids_select.png} - \label{img:blobs_border_select} \caption{$\delta$-Medoids choosing the border samples of a cluster} + \label{img:blobs_border_select} \end{figure} Selecting the border samples clearly was not intended and the $\delta$-Medoids algorithm had to select only the relevant samples. The relevance of a representative was determined to the number of points that fit into its $\delta$-neighborhood. \todo{where to list the changes, here or in the theoretical chapter, theory better I think} @@ -854,8 +864,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp2_blobs.png} - \label{img:exp2_blobs} \caption{Confusion matrices for Blobs 3D dataset} + \label{img:exp2_blobs} \end{figure} \textbf{Overlap} @@ -873,8 +883,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp2_overlap.png} - \label{img:exp2_overlap} \caption{Confusion matrices for Overlap dataset} + \label{img:exp2_overlap} \end{figure} \textbf{Moons} @@ -891,8 +901,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp2_moons.png} - \label{img:exp2_moons} \caption{Confusion matrices for Moons dataset} + \label{img:exp2_moons} \end{figure} \textbf{Circles} @@ -909,8 +919,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp2_circles.png} - \label{img:exp2_circles} \caption{Confusion matrices for Circles dataset} + \label{img:exp2_circles} \end{figure} @@ -919,8 +929,8 @@ \subsection{Conclusion} The results of this experiment empirically supported the expected result of not selecting the border samples from a dataset as can be seen on Figure~\ref{img:blobs_good_select} \begin{figure} \includegraphics[width=\linewidth]{img/delta_medoids_select_better.png} - \label{img:blobs_good_select} \caption{$\delta$-Medoids choosing only relevant samples of a cluster} + \label{img:blobs_good_select} \end{figure} This sanity check by visualization fulfilled all of the expectation. @@ -965,8 +975,8 @@ \subsection{Results} \begin{figure} \includegraphics[width=\linewidth]{img/exp3_pendigits.png} - \label{img:exp3_pendigits} \caption{Confusion matrices for Pendigits dataset} + \label{img:exp3_pendigits} \end{figure} \subsection{Conclusion} diff --git a/text/img/centroids.png 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z{|tC}fCYMUvMk}5BNDF_N7b+b$$dAI7bwy1hX2%qpyTn=l1GK?Pefi-)rXx3t;8C@ zqK80nt@nLwK+-`>t-cBthNGY4d`bjLM22)7E3^$cAjhi|%_E*h==VOs4@ud>8nO~I yzADNPl>TB7A$42z@2RWxf1A>Nhll^~4Xg`t;$IYYh#;APC4|vFk1jjoc>P~9jv|@> literal 0 HcmV?d00001 From 05563d911de9602b1b03dd407ac2fa6dc0a2e19a Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 10 May 2019 11:40:46 +0200 Subject: [PATCH 45/65] updated experiments pictures - not final probably rhough --- text/hlavac-thesis.tex | 40 +++++++++++++++++++++------------------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 5953432..e743649 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -740,9 +740,10 @@ \subsection{Experimental Setup} \subsection{Results} The results for each dataset are listed below. + \textbf{Blobs 3D} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -753,15 +754,15 @@ \subsection{Results} \caption{Number of selected representatives for Blobs 3D dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp1_blobs.png} \caption{Confusion matrices for Blobs 3D dataset} \label{img:exp1_blobs} -\end{figure} +\end{figure} \todo{generate pictures with cluster names rathern then numbers, or do i have to?} \textbf{Overlap} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -772,7 +773,7 @@ \subsection{Results} \caption{Number of selected representatives for Overlap dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp1_overlap.png} \caption{Confusion matrices for Overlap dataset} \label{img:exp1_overlap} @@ -780,7 +781,7 @@ \subsection{Results} \textbf{Moons} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -790,7 +791,7 @@ \subsection{Results} \caption{Number of selected representatives for Moons dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp1_moons.png} \caption{Confusion matrices for Moons dataset} \label{img:exp1_moons} @@ -798,7 +799,7 @@ \subsection{Results} \textbf{Circles} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -808,7 +809,7 @@ \subsection{Results} \caption{Number of selected representatives for Circles dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp1_circles.png} \caption{Confusion matrices for Circles dataset} \label{img:exp1_circles} @@ -849,9 +850,10 @@ \subsection{Experimental Setup} \subsection{Results} The results for different methods are listed below. + \textbf{Blobs 3D} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -862,7 +864,7 @@ \subsection{Results} \caption{Number of selected representatives for Blobs 3D dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp2_blobs.png} \caption{Confusion matrices for Blobs 3D dataset} \label{img:exp2_blobs} @@ -870,7 +872,7 @@ \subsection{Results} \textbf{Overlap} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -881,7 +883,7 @@ \subsection{Results} \caption{Number of selected representatives for Overlap dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp2_overlap.png} \caption{Confusion matrices for Overlap dataset} \label{img:exp2_overlap} @@ -889,7 +891,7 @@ \subsection{Results} \textbf{Moons} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -899,7 +901,7 @@ \subsection{Results} \caption{Number of selected representatives for Moons dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp2_moons.png} \caption{Confusion matrices for Moons dataset} \label{img:exp2_moons} @@ -907,7 +909,7 @@ \subsection{Results} \textbf{Circles} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -917,7 +919,7 @@ \subsection{Results} \caption{Number of selected representatives for Circles dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp2_circles.png} \caption{Confusion matrices for Circles dataset} \label{img:exp2_circles} @@ -955,7 +957,7 @@ \subsection{Results} \textbf{Pendigits} -\begin{table}[] +\begin{table}[H] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -973,7 +975,7 @@ \subsection{Results} \caption{Number of selected representatives for Pendigit dataset} \end{table} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp3_pendigits.png} \caption{Confusion matrices for Pendigits dataset} \label{img:exp3_pendigits} From d2faa73b38035fdf0cf8bc09d6721d47bd1bdd83 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 10 May 2019 12:10:20 +0200 Subject: [PATCH 46/65] added experiment 4 template and few notes to it --- text/hlavac-thesis.tex | 45 ++++++++++++++++++++++++++++++++++++++++- text/img/exp4.png | Bin 0 -> 105060 bytes 2 files changed, 44 insertions(+), 1 deletion(-) create mode 100644 text/img/exp4.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index e743649..eaf114f 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -697,7 +697,7 @@ \section{Image Recognition Datasets} The Multiple Features Data Set \cite{dua1998pendigit} consist of \todo{explain how is it different} \todo{add MNIST Fashion - if works remove multiple features??? Even though I have results.?} -\section{Real Network Dataset} +\section{Real Network Dataset}\label{sec:real-data} Real network traffic captured from a company with approximately 20 thousand employees. The capture is from 24 hours of a working day a consists of \todo{count flows} flows. Proxy logs were used to capture the connections. @@ -986,6 +986,49 @@ \subsection{Conclusion} An interesting thing is that Greedy Selection and $\delta$-Medoids One-Shot select similar number of representatives and their results are also very similar. This is interesting as the complexity of these algorithms differs a lot, Greedy Selection being much slower with $\mathcal(n^2)$. \todo{better wording of this conclusion} +\section{Experiment 4}\label{sec:exp4} + + +\subsection{Experimental Setup} +For this experiment clustered hosts from a real network were used. +This dataset is explained in greater detail in Section~\ref{sec:real-data}. +From this dataset traffic for first 4 hours was used for training the model and the next 4 hours were used for testing the representatives. +The similarity measure from Section~\ref{sec:ctadf} was used. + +All 4 algorithms were run. +The $\delta$ was set to 0.8 as was the threshold used in community-based clustering for their creation. + +\subsection{Results} + +The results for different methods are listed below. + +%\begin{table}[H] +%\begin{tabular}{l|lllll} +%\hline +% \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +%0 & 780 & 33 & 33 & 33 & 33 \\ +%1 & 779 & 33 & 41 & 41 & 41 \\ +%2 & 780 & 17 & 15 & 15 & 15 \\ +%3 & 719 & 12 & 9 & 9 & 9 \\ +%4 & 780 & 25 & 24 & 24 & 24 \\ +%5 & 720 & 25 & 27 & 27 & 27 \\ +%6 & 720 & 16 & 16 & 16 & 16 \\ +%7 & 778 & 22 & 20 & 20 & 20 \\ +%8 & 719 & 75 & 77 & 15 & 15 \\ +%9 & 719 & 50 & 52 & 13 & 13 \\ \hline +%\end{tabular} +%\caption{Number of selected representatives for Pendigit dataset} +%\end{table} +% +\begin{figure}[H] + \includegraphics[width=\linewidth]{img/exp4.png} + \caption{Confusion matrices for Pendigits dataset} + \label{img:exp4} +\end{figure} \todo{here add better cluster names for sure} + +\subsection{Conclusion} +The results clearly show that the $\delta$-Medoids algorithm is significanly better than selecting random representatives from the cluster. +The downside 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zvzDvE8-2;B3|X+GkXCX%-1iE!zQ=ld6bg)12~v!~UK$rCO)lScRx?moDR6`DirqjL1tX!o z#z7(j%IB2EXK-COY?Ty{Jdc`h7_WPSwI417qVKaI*6tp&RGOg7o{<+y2JS#D4{F~z zC$%`jn!^lLGnE7+;;?DWnl)VVgOg1?;$(js4?dp8W@hg~cfntUKhcRyvl7U|6}u}( x(jJ%m9R2>8ga*ui)%#Z#YT}sre_bru;rSP-e2yWmnuCCsiIEwi_=3xW{{r%+l>q<% literal 0 HcmV?d00001 From c042c221de83cd1646ae672d522ae896c26659bc Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 10 May 2019 14:49:02 +0200 Subject: [PATCH 47/65] experiments updated --- text/hlavac-thesis.tex | 55 +++++++++++++++++++------------- text/img/exp3_mnist_fashion.png | Bin 0 -> 111594 bytes 2 files changed, 33 insertions(+), 22 deletions(-) create mode 100644 text/img/exp3_mnist_fashion.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index eaf114f..49a8451 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -1002,33 +1002,44 @@ \subsection{Results} The results for different methods are listed below. -%\begin{table}[H] -%\begin{tabular}{l|lllll} -%\hline -% \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -%0 & 780 & 33 & 33 & 33 & 33 \\ -%1 & 779 & 33 & 41 & 41 & 41 \\ -%2 & 780 & 17 & 15 & 15 & 15 \\ -%3 & 719 & 12 & 9 & 9 & 9 \\ -%4 & 780 & 25 & 24 & 24 & 24 \\ -%5 & 720 & 25 & 27 & 27 & 27 \\ -%6 & 720 & 16 & 16 & 16 & 16 \\ -%7 & 778 & 22 & 20 & 20 & 20 \\ -%8 & 719 & 75 & 77 & 15 & 15 \\ -%9 & 719 & 50 & 52 & 13 & 13 \\ \hline -%\end{tabular} -%\caption{Number of selected representatives for Pendigit dataset} -%\end{table} -% +\begin{table}[H] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +1024 & 72 & 0 & 8 & 8 & 8 \\ +865 & 89 & 0 & 28 & 28 & 28 \\ +323 & 2125 & 0 & 821 & 40 & 40 \\ +68 & 116 & 0 & 82 & 36 & 36 \\ +645 & 83 & 0 & 56 & 56 & 56 \\ +1004 & 46 & 0 & 38 & 38 & 38 \\ +43 & 55 & 0 & 31 & 31 & 31 \\ +1196 & 835 & 0 & 441 & 34 & 34 \\ +253 & 59 & 0 & 47 & 47 & 47 \\ +112 & 77 & 0 & 66 & 66 & 66 \\ +946 & 2065 & 0 & 781 & 30 & 30 \\ +116 & 238 & 0 & 71 & 17 & 17 \\ +393 & 51 & 0 & 38 & 38 & 38 \\ +756 & 127 & 0 & 92 & 34 & 34 \\ +124 & 67 & 0 & 48 & 48 & 48 \\ +221 & 50 & 0 & 50 & 50 & 50 \\ \hline +\end{tabular} +\caption{Number of selected representatives for real network data} +\end{table} +\todo{greedy medoids jeste pocitam} + \begin{figure}[H] \includegraphics[width=\linewidth]{img/exp4.png} - \caption{Confusion matrices for Pendigits dataset} + \caption{Confusion matrices for real network data} \label{img:exp4} -\end{figure} \todo{here add better cluster names for sure} +\end{figure} +\todo{here add better cluster names for sure} +\todo{get precision and recall values per cluster} \subsection{Conclusion} -The results clearly show that the $\delta$-Medoids algorithm is significanly better than selecting random representatives from the cluster. -The downside is the speed of the algorithm. \todo{expand the conclusion} +The results show that the $\delta$-Medoids algorithm is significantly better than selecting random representatives from the cluster. +Even for clusters with several thousand of samples few samples were selected a while retaining good properties for assigning points to clusters. +The downside is the speed of the algorithm but this was not a part of this experiment. +\todo{expand the conclusion} \setsecnumdepth{part} diff --git a/text/img/exp3_mnist_fashion.png b/text/img/exp3_mnist_fashion.png new file mode 100644 index 0000000000000000000000000000000000000000..bc9b1539ca7aa379d0897fc4eaa591676d6feec0 GIT 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zMkH7}I=u>l6_d$4lG$xo+CQXUFPT=PtEw6AwO>=B6GwjC%IcNO6*7ia@F3{HNOg85x$(A|p^PJ53u3H+?%0nuh44i`J}1OQgj&b+^8j z5(h#(wAJ5%H08y?luCq#-baj~c*I%qSxDu~O;==)&JkMsw)_OC3d&PMvT}c`{^8Hd fi39)hrJUNJ!`EUfCNR?w2FadXe%`fTho%1o40H@- literal 0 HcmV?d00001 From de41cc6c35d0d501b73efeb67056ab2b80d14599 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Fri, 10 May 2019 18:20:35 +0200 Subject: [PATCH 48/65] added kmeans pseudocode --- text/hlavac-thesis.tex | 133 ++++++++++++++++++++++------------------- 1 file changed, 73 insertions(+), 60 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 49a8451..02c46fa 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -81,7 +81,7 @@ \chapter{Introduction} Each connected device is an opportunity for attackers. Security engineers are always inventing new methods to stop the attacks and keep their networks safe and secure. It is a never-ending race between attackers, trying to overcome new security methods, and defenders, the security personnel, trying to be always one step ahead of the attackers. -This race creates the drive for more and more sophisticated defense mechanisms. \\ +This race creates the drive for more and more sophisticated defense mechanisms. Multi-layer defense systems are protecting contemporary networks. As attackers overcome one layer, there is yet another layer waiting for them. @@ -95,22 +95,22 @@ \chapter{Introduction} One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). In NBAD, devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. The method then compares the current behavior of the network to a model created from the historical data. -If a significant deviation in behavior occurs, the security team is notified and can act accordingly. \\ +If a significant deviation in behavior occurs, the security team is notified and can act accordingly. It is not uncommon for a company to have tens of thousands of devices and even more. Straight forward usage of NBAD in these networks is not sufficient to detect breaches successfully. For example, infection of a single device can be missed in the traffic of a medium sized company. In order to detect such anomalies, it is needed to focus on smaller parts of the network. -Behavioral clustering of network hosts can be used to separate it into smaller pieces. \\ +Behavioral clustering of network hosts can be used to separate it into smaller pieces. In Cognitive targeted anomaly detection framework from Cisco, behavioral clustering is used to find groups of similar hosts in a network. Each cluster created consists of hosts that have similar behavior. The main focus of this work is to find a representation of these clusters that are used to calculate the model behavior. The representation used in this thesis is based on cluster prototypes, which will be described in Chapter~\ref{ch:theory} -These cluster prototypes are then updated dynamically as the behavior of the network changes. \\ +These cluster prototypes are then updated dynamically as the behavior of the network changes. Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. -They were tested and compared on both real and test datasets. \\ +They were tested and compared on both real and test datasets. The thesis is organized as follows. Chapter~\ref{ch:ids} introduces the anomaly-based network security. @@ -120,12 +120,12 @@ \chapter{Introduction} %The main focus of this work is in representing these clusters found by clustering algorithms. %Finding representation of clusters in Cognitive targeted anomaly detection framework is made more difficult by the fact, that similarity measure used for clustering does not form a metric space. -%Therefore the focus on methods that can be used for selecting representatives in non-metric spaces. \\ +%Therefore the focus on methods that can be used for selecting representatives in non-metric spaces. % %Each cluster can have up to several thousands of hosts. %Algorithms considered in this thesis were selected based on the following expectations. %As the hosts are clustered based on their behavior, it is safe to expect that selecting a subset of the cluster can represent its behavior without losing significant information. -%Furthermore having fewer hosts as representatives of each cluster significantly reduce memory usage. \\ +%Furthermore having fewer hosts as representatives of each cluster significantly reduce memory usage. %Why use clustering methods in network anomaly detection? %Why do we need representative selection? @@ -145,17 +145,14 @@ \chapter{Goals} This work aims to study methods used for the representation of network host behavioral clusters that can be applied in Cognitive targeted anomaly detection framework. Behavioral similarity used for clustering in this framework does not form a metric space, which creates a requirement for algorithms used for representative selection. -\medskip This thesis should present a method that can be used for finding the cluster prototypes of clusters in non-metric space. These cluster prototypes represent the whole cluster in a way that is convenient for further computation. -It can be, for example, assigning newly added samples to their corresponding cluster.\\ -\medskip +It can be, for example, assigning newly added samples to their corresponding cluster. Networks monitored by Cognitive targeted anomaly detection framework can have up to several hundreds of thousands of hosts which leads to massive clusters. If the classification of newly added hosts were done by comparing the new host to all others, the memory and time needed for classification would be unmanageable. -That is why the number of representatives selected from each cluster should be kept as small as possible. \\ -\medskip +That is why the number of representatives selected from each cluster should be kept as small as possible. General methods used for general cluster representation in non-metric spaces should be studied. From these previously studied methods, the best ones will be selected and implemented. @@ -174,7 +171,7 @@ \chapter{Intrusion Detection System}\label{ch:ids} Different approaches to network intrusion detection systems (IDS) are explained in Section~\ref{sec:ids}. In Section~\ref{sec:nbad}, the scope is narrowed to network anomaly detection IDS and Section~\ref{sec:challenges}delves deeper into the challenges of it. Section~\ref{sec:ctadf} serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. -Methods researched in this thesis were tested on real data collected from Cognitive targeted anomaly detection framework. \\ +Methods researched in this thesis were tested on real data collected from Cognitive targeted anomaly detection framework. \section{Types of Intrusion Detection Systems}\label{sec:ids} @@ -211,7 +208,7 @@ \section{Network Anomaly Detection}\label{sec:nbad} This method focuses on comparing network host behavior changes in time. If a change more significant than a certain threshold is observed, network anomaly is detected. In IDS, when an anomaly is detected, an alert is created to inform the network administrator about what has happened. -This alert can be any kind of message ranging from a syslog message to an email sent to the admin. \\ +This alert can be any kind of message ranging from a syslog message to an email sent to the admin. Host behavior is collected from devices in the network. There is at least one device (although many times multiple) dedicated for collection of network flows (using NetFlow protocol). @@ -220,31 +217,31 @@ \section{Network Anomaly Detection}\label{sec:nbad} This aggregation of information enables much faster (even real-time) detection of problems on a network as compared to, for example, deep packet inspection (DPI). DPI is another method used to detect problems in a network. It focuses on exploring the payload of each packet and is mostly considered unusable. -Not only the majority of traffic is encrypted, but it is also impossible to look into each packet because of the enormous amount of traffic in today's networks. \\ +Not only the majority of traffic is encrypted, but it is also impossible to look into each packet because of the enormous amount of traffic in today's networks. Network flows are collected and then sent via NetFlow protocol to one place where they are stored, and evaluated by detection algorithms. Network anomaly detection system creates a baseline model of the network behavior, which is then compared to the actual traffic. -Any sudden change is considered to be an anomaly that is reported by the IDS. \\ +Any sudden change is considered to be an anomaly that is reported by the IDS. \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} Even if it was possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100\% precision. Not every network host generates enough traffic to create a precise model of its behavior. -Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. \\ -\todo{Explain better, what is the goal of anomaly detection, Grill Thesis} +Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. +\todo{Explain better, what is the goal of anomaly detection, Grill Thesis - toto ma ted malou prioritu} That is why models are calculated for whole networks where it is possible to create a model that reflects the behavior of the network. This method is widely used, and on smaller (several devices) networks it is very efficient. However, capturing flow data from a big network of thousands of devices can result in too much information. One can easily miss one infected device generating different traffic. -The noise in the collected data is too high. \\ +The noise in the collected data is too high. To give an example, imagine detection of a single infected device, such as a printer in a 10 story office building. This printer, being a part of a botnet, was ordered to generate traffic for DDoS (Distributed Denial of Service) attacks. Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. -But an anomaly detection system could hardly notice a change when considering the traffic of the whole office building. \\ +But an anomaly detection system could hardly notice a change when considering the traffic of the whole office building. If an AD system does not mark malicious traffic as an anomaly, it is considered a false negative. -That is another challenge AD systems are struggling with, to keep the number of missed attacks at 0 or as close to it as possible.\\ +That is another challenge AD systems are struggling with, to keep the number of missed attacks at 0 or as close to it as possible. One more problem connected to false negatives are the false positives. A false positive is a network sample of benign network traffic marked as anomalous by the AD system. @@ -254,7 +251,7 @@ \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} There are many other situations when false positive alerts can happen. Having more than 0 false positives is not that big of a problem as having more than 0 false negatives. In IDS there is always a way to filter and double check the anomalies. -However, a network anomaly detection system should avoid a big overhead in false positives as it could overwhelm the system.\\ +However, a network anomaly detection system should avoid a big overhead in false positives as it could overwhelm the system. In conclusion, a good anomaly detection system should detect all anomalies that are somehow connected to malicious behavior while trying to keep the number of false positives alerts as small as possible. The small number of false positives that are reported is then double-checked in the following layers in the IDS. @@ -266,11 +263,11 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} The aim is to split the whole network into smaller groups. Running anomaly detection for each group then yields significantly better results as compared to the traditional whole network approach. Not only it works better because of community-based clustering, but also because of the ability to adapt dynamically as the network changes. -Thus it is able to incorporate changes that happen on a given network such as adding and removing devices.\\ +Thus it is able to incorporate changes that happen on a given network such as adding and removing devices. The method is separated into two phases. In the initial phase, the state of the network is learned, and an initial model is created. -Then, in the second ongoing phase, the framework dynamically adjusts clusters to the current state of the network. \\ +Then, in the second ongoing phase, the framework dynamically adjusts clusters to the current state of the network. The initial phase starts by collecting 24 hours of traffic from a given network. Collected data consists of network traffic flows and proxy server logs. @@ -285,33 +282,33 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} F_s^h = \frac{i}{n}\sum\limits_{1}{n}I(t_i, s, h)\:, \end{equation} where $n$ is the number of time windows, $I$ is the indicator function, which is 1 if the network host $h$ visited the server $s$ in the timewindow $t_i$ and 0 otherwise. -This ratio-based frequency ensures that frequently visited servers (e.g., Google, Facebook) do not overshadow the less frequently visited servers.\\ +This ratio-based frequency ensures that frequently visited servers (e.g., Google, Facebook) do not overshadow the less frequently visited servers. When each host is represented, the clustering algorithm is started. A technique called community-based clustering is used to create groups of hosts. This clustering works for graphs, where communities are more densely connected parts of the graph. In the words of this NBAD: Those hosts that communicate with similar peers are considered to be in the same community. -Community-based clustering is explained in detail in Section~\ref{sec:cluster_topo}.\\ +Community-based clustering is explained in detail in Section~\ref{sec:cluster_topo}. The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: \begin{equation} \textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:, \end{equation} -where $F_a$, $F_b$ represent the frequency and $S$ represents the union of sets of servers visited by the network hosts $a$ and $b$. \\ +where $F_a$, $F_b$ represent the frequency and $S$ represents the union of sets of servers visited by the network hosts $a$ and $b$. After the clustering is done, only clusters that are bigger than 10 hosts are selected for representative selection. Smaller clusters are analyzed using a fallback method - host-centered anomaly detection. -For the purpose of this work, we do not consider clusters smaller than 10 hosts. \\ +For the purpose of this work, we do not consider clusters smaller than 10 hosts. When clusters are determined, $\kappa$ random representatives are selected from each cluster. Currently, $\kappa$ is an empirically set parameter. In this thesis, tests were made to improve this random selection method. -A more detailed explanation of this method can be found in \cite{kopp2018community}.\\ +A more detailed explanation of this method can be found in \cite{kopp2018community}. After the initial training phase is over and the model is established, data continues to be collected. Every 4 hours all hosts that are observed are assigned to existing clusters. -At that point, a portion of cluster representatives is replaced by new ones to capture the ever-changing nature of the network data. \\ +At that point, a portion of cluster representatives is replaced by new ones to capture the ever-changing nature of the network data. These clusters are then further used in anomaly detection. They serve as a foundation for calculating baseline behavior for hosts belonging to it. @@ -333,7 +330,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} \mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:, \end{equation} \todo{are these equation numbers-corresponding to sections correct?} where \textit{e} is an object from given cluster. -The distance is a measure that quantifies how far two objects with the same features are. \todo{better wording here} +The distance is a measure that quantifies the proximity of two objects with the same number of features. Many different distance measures are used in clustering. Several of commonly used ones are: \begin{itemize} @@ -342,6 +339,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} \item Manhattan distance \item Cosine similarity \end{itemize} +Cosine similarity measures the angle between two feature vectors. Dissimilarity is defined as \begin{equation} @@ -369,21 +367,35 @@ \section{Introduction to Clustering}\label{sec:clust_intro} After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. The algorithm stops when the centroids of clusters stop changing. Figure~\ref{alg:kmeans} shows the pseudocode of $K$-means. -\begin{algorithm}\label{alg:kmeans} - \caption{K-Means pseudocode} +\begin{algorithm}[H]\label{alg:kmeans} + \caption{K-Means} \label{k_mean_pseudocode} \begin{algorithmic}[1] - \INPUT set of examples $X$; number of clusters $k$ - \STATE Randomly chose $k$ examples from $X$ as centroids - \WHILE{True} - \STATE Create $k$ clusters by assigning each object to the closest centroid - \STATE Compute $k$ new centroids by averaging objects in each cluster - \IF{Centroids do not change} - \STATE \textbf{break} - \ENDIF - \ENDWHILE + \INPUT data $X = x_0, x_1, ..., x_n$; number of clusters $k$ + \OUTPUT $k$ clusters; $k$ centroids + \STATE $t = 0$ + \STATE Initialize $centroids_t$ = $k$ randomly chosen examples from $X$ + \DO + \STATE $t = t + 1$ + \STATE Initialize $clusters = \emptyset$ + \FOR{$c$ in $centroids_t$} + \STATE Initialize $cluster_c = \emptyset$ + \STATE add $cluster_c$ to $clusters$ + \ENDFOR + \FOR{$x$ in $X$} + \STATE $closest\_centroid = \mathrm{argmin}_{c \in centroids} d(c, x)$ + \STATE add $x$ to $cluster_{closest\_centroid}$ + \ENDFOR + \STATE $centroids_t = \emptyset$ + \FOR{$cluster$ in $clusters_t$} + \STATE $new\_centroid = \mathrm{mean}(cluster)$ + \STATE add $new\_centroid$ to $centroids_t$ + \ENDFOR + \DOWHILE{$centroids_t = centroids_{t-1}$} + \STATE \textbf{return} $clusters$, $centroids_t$ + \end{algorithmic} -\end{algorithm} \todo{better pseudocode, find better book} +\end{algorithm} \todo{is this pseudocode looking better} This algorithm is fast, it has time complexity $\mathcal{O}(k \cdot n)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. It is the most common clustering algorithm as it typically converges in a few iterations. @@ -490,7 +502,7 @@ \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_met Selecting a prototype from this dataset is often best achieved by calculating a centroid for a given cluster. A centroid can be calculated as the mean of the points in the cluster. An example of a centroid in a cluster can be found in Figure~\ref{img:centroids} a). -Other ways of calculating medoids can be used, e.g. weighted average of all points. \\ +Other ways of calculating medoids can be used, e.g. weighted average of all points. \begin{figure} \includegraphics[width=\linewidth]{img/centroids.png} @@ -498,12 +510,12 @@ \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_met \label{img:centroids} \end{figure} -However, if a calculated centroid is not meaningful (see Figure~\ref{img:centroids} b)\todo{references not working? why?}), a medoid can be selected as an alternative to it. +However, if a calculated centroid is not meaningful (see Figure~\ref{img:centroids} b)), a medoid can be selected as an alternative to it. A medoid is a point that is in the set that minimizes the average distance to all the other points in the set. It can be thought of as a median of the dataset \todo{this is good for an explanation, but not very scientific, as it is not entirely true, how to explain better?}. Formally, medoid is defined as: \begin{equation} -x_{medoid} = \mathrm{argmin}_{y in \{x_1, x_2, ..., x_n\}} \sum_{i=1}^{n}{d(y, x_i)}\:, +x_{medoid} = \mathrm{argmin}_{y \in \{x_1, x_2, ..., x_n\}} \sum_{i=1}^{n}{d(y, x_i)}\:, \end{equation} where $x_1, x_2, ..., x_n$ is a set of $n$ points in a space with a distance function $d$. \todo{definition taken from wikipedia - similar is in xu2011network but not that nice - how to cite?? } In Figure~\ref{img:medoids} medoids were chosen instead of centroids. @@ -541,11 +553,11 @@ \section{Algorithms Relevant for Topological Space}\label{sec:relevant_methods} \subsection{Random Selection}\label{subsec:random_select} Random selection algorithm serves as a baseline to measure the improvement as it is the method currently used in Cognitive targeted anomaly detection framework. -It selects a given number of representatives from the cluster randomly. \\ +It selects a given number of representatives from the cluster randomly. In clusters dense clusters, selecting random samples from each cluster leads to undesired noise. However as experimental results presented in \cite{kopp2018community} in the domain of computer networks the density of clusters is not significant. -It, of course, depends on each network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. \\ +It, of course, depends on each network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. For this thesis, the random selection algorithm serves as a baseline in comparing the results to other methods. @@ -554,14 +566,14 @@ \subsection{Greedy Selection}\label{subsec:greedy_select} The greedy selection algorithm is a straightforward algorithm that selects a random sample from the set as the first representative. Then it looks for the most different sample in the rest of the dataset. Once the desired sample is found, the algorithm removes all neighboring samples with at least the similarity of $\delta$ from the set. -Then it looks for other dissimilar samples until the whole set is represented. \\ +Then it looks for other dissimilar samples until the whole set is represented. The speed of this algorithm depends on the sparsity of the given set. In the worst case scenario, all points would be selected as representatives while always calculating the distance to each remaining sample in the set. Then it would reach the time complexity of $\mathcal{O}(n^2)$. \subsection{$\delta$-Medoids}\label{subsec:delta_medoids} The two following algorithms were first eplained in \cite{liebman2015representative}. -They were tested on two different problems that also use clustering in non-metric space - computing distance of two musical segments and comparing trajectories of objects.\\ +They were tested on two different problems that also use clustering in non-metric space - computing distance of two musical segments and comparing trajectories of objects. $\delta$-Medoids algorithm tries to find a minimal subset of points that are needed to cover the cluster with only one given constraint, that distance of the representatives would be at least $\delta$. There are two versions of this algorithm. @@ -570,7 +582,7 @@ \subsection{$\delta$-Medoids}\label{subsec:delta_medoids} \subsection{$\delta$-Medoids One-Shot} The idea behind this algorithm is to go through the dataset looking for representatives that differ at least by the given parameter $\delta$ from each previously selected representatives. -By taking this approach, it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. \\ +By taking this approach, it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. The algorithm is shown in Figure~\ref{alg:delta_medoids_one_shot}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. @@ -604,7 +616,7 @@ \subsection{$\delta$-Medoids} The full version of the algorithm runs a one-shot algorithm multiple times. Each time it goes through the dataset it selects a better medoid than before. Once two passes through the data do not change any medoid, the algorithm stops. -This algorithm is shown in Figure~\ref{alg:delta_medoids_full}.\\ +This algorithm is shown in Figure~\ref{alg:delta_medoids_full}. This approach is slower, as the main routine from one shot algorithm has to be run multiple times. \todo{Mention convergence of the algorithm.} @@ -646,18 +658,19 @@ \subsection{$\delta$-Medoids} \DOWHILE{$representatives_t = representatives_{t-1}$} \end{algorithmic} \end{algorithm} -\todo{add my improvement to the pseudocode} +\todo{add my improvement to the pseudocode - omezeni ze jeden reprezentant musi zabirat minimalne 1 procento vsech bodu, take reprezentantu nesmi byt vice nez 50 nebo vice nez 10 procent celeho clusteru} %========================================CHAPTER================================== \chapter{Datasets}\label{ch:datasets} The task of cluster representation in non-metric spaces generally is not necessarily connected only to the security field. -That is why, before moving to a dataset collected from the real network, the selected algorithms were first tested on artificially created datasets and well labeled datasets found in the literature. \\ +That is why, before moving to a dataset collected from the real network, the selected algorithms were first tested on artificially created datasets and well labeled datasets found in the literature. Below, the datasets used in this thesis are presented. There are three types of datasets used: artificially created, image recognition and real network dataset. -Each section corresponds to one of the listed types. \\ \todo{is this introduction enough? What is missing?} +Each section corresponds to one of the listed types. +\todo{is this introduction enough? What is missing?} \section{Datasets Created for this Work} @@ -676,7 +689,7 @@ \section{Datasets Created for this Work} \caption{Graphs of all datasets} \label{img:datasets} \end{figure} -Similarity measure used in these datasets was Euclidean distance. \\ +Similarity measure used in these datasets was Euclidean distance. The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter X. \todo{Link to chapter} The Overlap dataset consists of 3 clusters in shapes of blocks. These blocks partly overlap to test how selected algorithms cope with overlapping clusters. @@ -687,7 +700,7 @@ \section{Datasets Created for this Work} \section{Image Recognition Datasets} One of the best-documented areas of clustering is the area of image recognition. That is why two datasets from this area were chosen for measuring the precision and complexity of each method. -Both of them are labeled datasets of features collected from digits written by human hand. \\ +Both of them are labeled datasets of features collected from digits written by human hand. The Pen-Based Recognition of Handwritten Digits Data Set \cite{dua1998pendigit} consists of 10922 samples with 16 attributes. Each sample represents features collected from a handwritten digit. @@ -702,7 +715,7 @@ \section{Real Network Dataset}\label{sec:real-data} The capture is from 24 hours of a working day a consists of \todo{count flows} flows. Proxy logs were used to capture the connections. Hosts that appeared in that capture were clustered using the method explained in Section~\ref{sec:cluster_topo}. -These clusters with all hosts labeled were used as real network dataset in this thesis.\\ +These clusters with all hosts labeled were used as real network dataset in this thesis. %========================================CHAPTER================================== @@ -1048,11 +1061,11 @@ \subsection{Conclusion} \chapter{Conclusion} Literature in both Network Anomaly Detection field and representative selection was studied. Approaches and algorithms thus found were combined and implemented in prototype program. -Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. \\ +Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. For testing the selected method datasets were created and gathered from the internet to create a diverse testbed for comparing algorithms. From the selected algorithms both $\delta$-Medoids One Shot and $\delta$-Medoids outperformed Random selection of representatives, which is the current solution of representatives selection in the framework. -The improvement was at least by 1\% in each dataset, while average improvement was 2\%. \todo{Double check these numbers.} \\ +The improvement was at least by 1\% in each dataset, while average improvement was 2\%. \todo{Double check these numbers.} \todo{Fine-tune the algorithms for the NBAD field and write here about it if it works.} From fffbd511a1fbc76a18e497893b3ad03afc9772ca Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sat, 11 May 2019 12:07:28 +0200 Subject: [PATCH 49/65] final version w\o abstract, conclusion and time complexity --- text/hlavac-thesis.tex | 90 ++++++++++++++++++++++++----- text/img/exp-5cluster-coverage.png | Bin 0 -> 15580 bytes text/img/exp4.png | Bin 105060 -> 120379 bytes 3 files changed, 77 insertions(+), 13 deletions(-) create mode 100644 text/img/exp-5cluster-coverage.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 02c46fa..211d13f 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -452,6 +452,7 @@ \section{Clustering on Topological Space}\label{sec:cluster_topo} If samples differ more than a predefined threshold, a new cluster prototype is created. \section{Representative Selection} +\label{sec:representative_selection} This section focuses on the idea of finding a representation of clusters. Clustering huge datasets can result in big clusters of several tens of thousands of objects in them. @@ -482,7 +483,7 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ \end{itemize} To compare the quality of different subsets returned by different algorithms, two criteria are measured: \begin{itemize} - \item \textbf{Criterion 1}: $|Z|$ - seeking the smallest possible subset $Z$ that satisfies Requirement 1:. + \item \textbf{Criterion 1}: $|Z|$ - seeking the smallest possible subset $Z$ that satisfies Requirement 1. \item \textbf{Criterion 2}: Representative should best fit the data on average. Given representative subsets of equal size, the preference is on the one that minimizes the average distance of samples from their respective representatives. \end{itemize} Criteria 1 and 2 are applied to a representative set solution. @@ -587,9 +588,9 @@ \subsection{$\delta$-Medoids One-Shot} The algorithm is shown in Figure~\ref{alg:delta_medoids_one_shot}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. -\begin{algorithm}\label{alg:delta_medoids_one_shot} +\begin{algorithm} \caption{$\delta$-Medoids One-shot} - \label{delta_medoids_one_shot} + \label{alg:delta_medoids_one_shot} \begin{algorithmic}[1] \INPUT data $x_0$ ... $x_m$, required distance $\delta$ \STATE Initialize \textit{representatives} = $\emptyset$ @@ -617,17 +618,19 @@ \subsection{$\delta$-Medoids} Each time it goes through the dataset it selects a better medoid than before. Once two passes through the data do not change any medoid, the algorithm stops. This algorithm is shown in Figure~\ref{alg:delta_medoids_full}. +Original algorithm from \cite{liebman2015representative} lacks the routine \textit{ReduceClusters} on line 24. +This routine is introduced in this thesis as an improvement of the algorithm because of the algorithm properties found in Section ~\ref{sec:exp1}. This approach is slower, as the main routine from one shot algorithm has to be run multiple times. -\todo{Mention convergence of the algorithm.} +Experimental results of \cite{liebman2015representative} show that the algorithm converges in very few (<10) cycles. On the other hand, it is able to select better medoids, thus getting rid of the difficult choice of the first representative to select. -\begin{algorithm}\label{alg:delta_medoids_full} +\begin{algorithm} \caption{$\delta$-Medoids} - \label{delta_medoids_full} + \label{alg:delta_medoids_full} \begin{algorithmic}[1] \INPUT data $x_0$ ... $x_m$, required distance $\delta$ - \STATE t = 0 + \STATE $t = 0$ \STATE Initialize $representatives_{t_0} = \emptyset$ \STATE Initialize \textit{clusters} = $\emptyset$ \DO @@ -646,10 +649,11 @@ \subsection{$\delta$-Medoids} \ELSE \STATE \textit{representative = $x_i$} \STATE Initialize $cluster_{representative} = \emptyset$ - \STATE ad $x_i$ to $cluster_{representative}$ + \STATE add $x_i$ to $cluster_{representative}$ \STATE add $cluster_{representative}$ to \textit{clusters} \ENDIF \ENDFOR + \STATE Call \textbf{\textit{ReduceClusters}} \STATE Initialize $representatives_t = \emptyset$ \FOR{\textit{cluster} in \textit{clusters}} \STATE $representative = \textrm{argmin}_{s \in cluster} (\sum\limits_{x \in cluster}{d(x,s) : d(x,s) \le \delta)}$ @@ -658,7 +662,34 @@ \subsection{$\delta$-Medoids} \DOWHILE{$representatives_t = representatives_{t-1}$} \end{algorithmic} \end{algorithm} -\todo{add my improvement to the pseudocode - omezeni ze jeden reprezentant musi zabirat minimalne 1 procento vsech bodu, take reprezentantu nesmi byt vice nez 50 nebo vice nez 10 procent celeho clusteru} + +The subroutine \textit{ReduceClusters} shown in Figure~\ref{alg:reduce} helps to reduce the number of selected representatives to a number lower than a constant $\kappa$. +If a representative does not cover at least 1\% of the cluster, the routine tries to add him to the coverage of the representative that is similar to it. +If no such similar representative exists, it is considered noise and the representative is dropped from the cluster. + + +\begin{algorithm} + \caption{ReduceClusters} + \label{alg:reduce} + \begin{algorithmic}[1] + \INPUT $m$ subclusters $X$ = $x_i, ..., x_m$; their representatives $R = r_0, ..., r_m$; whole cluster size$s$ + \STATE $threshold = 0.01s$ + \STATE Initiate $i = 0$ + \IF{$|X| > \kappa$} + \WHILE{$i < m$} + \IF{$|x_i| \le threshold$} + \STATE $j$ = index the most similar representative from $R$ + \IF{no such $j$ exists} + \STATE Drop $x_i$ from $X$ + \ELSE + \STATE Merge $x_i$ and $x_j$. + \ENDIF + \STATE Drop $r_i$ from $R$ + \ENDIF + \ENDWHILE + \ENDIF + \end{algorithmic} +\end{algorithm} %========================================CHAPTER================================== @@ -835,7 +866,7 @@ \subsection{Conclusion} The number of missed classification is low even in overlapping or complex datasets. However, when the results were visualized for one cluster, the delta medoids algorithm did not result in expected selection. As can be seen on Figure~\ref{img:blobs_border_select}, the algorithm picked the samples on the edge of the dataset that did not cover many points in their neighborhoods. -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/delta_medoids_select.png} \caption{$\delta$-Medoids choosing the border samples of a cluster} \label{img:blobs_border_select} @@ -942,7 +973,7 @@ \subsection{Results} \subsection{Conclusion} The results of this experiment empirically supported the expected result of not selecting the border samples from a dataset as can be seen on Figure~\ref{img:blobs_good_select} -\begin{figure} +\begin{figure}[H] \includegraphics[width=\linewidth]{img/delta_medoids_select_better.png} \caption{$\delta$-Medoids choosing only relevant samples of a cluster} \label{img:blobs_good_select} @@ -957,8 +988,8 @@ \section{Experiment 3}\label{sec:exp3} \subsection{Experimental Setup} Datasets used for this experiment were: \begin{itemize} - \item Pendigit - \item + \item Pendigits + \item MNIST Fashion \end{itemize} All 4 algorithms were run. @@ -994,6 +1025,14 @@ \subsection{Results} \label{img:exp3_pendigits} \end{figure} +\textbf{MNIST Fashion} + +\begin{figure}[H] + \includegraphics[width=\linewidth]{img/exp3_mnist_fashion.png} + \caption{Confusion matrices for real network data} + \label{img:exp4} +\end{figure} \todo{table with representatives selected} + \subsection{Conclusion} Using algorithms on real datasets started to show more significant differences between results in algorithms. An interesting thing is that Greedy Selection and $\delta$-Medoids One-Shot select similar number of representatives and their results are also very similar. @@ -1045,6 +1084,7 @@ \subsection{Results} \caption{Confusion matrices for real network data} \label{img:exp4} \end{figure} + \todo{here add better cluster names for sure} \todo{get precision and recall values per cluster} @@ -1054,6 +1094,30 @@ \subsection{Conclusion} The downside is the speed of the algorithm but this was not a part of this experiment. \todo{expand the conclusion} + +\section{Experiment 5}\label{sec:exp5} + +In this experiment the average data fit as defined in Section~\ref{sec:representative_selection} was tested. + +\subsection{Experimental Setup} +In this experiment one cluster was represented by $\delta$-Medoids and Random Selection algorithms. +The cluster was selected from dataset\todo{dataset, multiple clusters?}. +After the representatives were selected, the percentage of coverage by each representative was calculated and plotted into graph. + + +\subsection{Results} + +\begin{figure}[H] + \includegraphics[width=\linewidth]{img/exp5.png} + \caption{Average fit of representatives in one cluster} + \label{img:exp5} +\end{figure} + +\subsection{Conclusion} +On average, the $\delta$-Medoids algorithm covers the cluster better with each added representative. +In the random selection, there is the possibility of selecting either the border cases or two points with overlapping neighborhoods. +This can occur even more often in data, that do not have standard distribution and can be denser in one part of a cluster than the other. + \setsecnumdepth{part} %========================================CHAPTER================================== diff --git a/text/img/exp-5cluster-coverage.png b/text/img/exp-5cluster-coverage.png new file mode 100644 index 0000000000000000000000000000000000000000..a9d8f60dea233085c9ae8d0ca6cc839262f30e45 GIT binary patch literal 15580 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\includegraphics[width=\linewidth]{img/exp5.png} - \caption{Average fit of representatives in one cluster} +%VH \includegraphics[width=\linewidth]{img/exp5.png} + \includegraphics[width=\linewidth]{img/DummyFigure.pdf} + \caption{\cbl Average fit of representatives in one cluster} \label{img:exp5} \end{figure} From 522f751387fa901461c39a348a4fa87fa5d3f7a0 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 12 May 2019 08:18:57 +0200 Subject: [PATCH 52/65] minor modifications --- text/hlavac-thesis.tex | 40 ++++++++++++++++++++++++++++++++++++---- text/img/exp5.png | Bin 0 -> 56593 bytes 2 files changed, 36 insertions(+), 4 deletions(-) create mode 100644 text/img/exp5.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 211d13f..e0c359c 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -612,7 +612,7 @@ \subsection{$\delta$-Medoids One-Shot} The choice of the first representative strongly influences the selection of the medoids to represent the cluster. In some cases, this one-shot approach could be misleading. -\subsection{$\delta$-Medoids} +\subsection{$\delta$-Medoids Full} The full version of the algorithm runs a one-shot algorithm multiple times. Each time it goes through the dataset it selects a better medoid than before. @@ -667,6 +667,9 @@ \subsection{$\delta$-Medoids} If a representative does not cover at least 1\% of the cluster, the routine tries to add him to the coverage of the representative that is similar to it. If no such similar representative exists, it is considered noise and the representative is dropped from the cluster. +\subsection{$\delta$-Medoids Modified} + +\todo{modified algorithm betterexplained} \begin{algorithm} \caption{ReduceClusters} @@ -691,6 +694,14 @@ \subsection{$\delta$-Medoids} \end{algorithmic} \end{algorithm} +The time complexity is of $\delta$-Medoids is shown on Figure~\ref{img:complexity}. + +\begin{figure} + \includegraphics[width=\linewidth]{img/complexity.png} + \caption{Comparing run times of $\delta$-Medoids and Random Selection} + \label{img:complexity} +\end{figure} + %========================================CHAPTER================================== \chapter{Datasets}\label{ch:datasets} @@ -716,7 +727,7 @@ \section{Datasets Created for this Work} All of these datasets are in space of two or three dimensions. They can be easily visualized on graphs and nicely show some of the specific properties of implemented algorithms. \begin{figure} - \includegraphics[width=\linewidth]{img/datasets.png} + \includegraphics[width=\linewidth]{img/datasets.png} \caption{Graphs of all datasets} \label{img:datasets} \end{figure} @@ -980,7 +991,7 @@ \subsection{Conclusion} \end{figure} This sanity check by visualization fulfilled all of the expectation. -\section{Experiment 3}\label{sec:exp3} +\section{Image Recognition Datasets}\label{sec:exp3} After the algorithms were tested on visualizable data, the experiments with well known labeled datasets could start. The motivation for this experiment was to test the algorithm results on well explored and well-annotated datasets commonly used as a benchmark for clustering. @@ -992,7 +1003,6 @@ \subsection{Experimental Setup} \item MNIST Fashion \end{itemize} -All 4 algorithms were run. The $\delta$ was estimated as \todo{how is median at lower ten percent called, put it to maths} times 1.05 from distances from one point in the data to all of the others. \subsection{Results} @@ -1027,6 +1037,28 @@ \subsection{Results} \textbf{MNIST Fashion} +In the experiment for this dataset the Greedy Selection algorithm was omitted as the experiments would take too long on clusters that contain 6000 samples. +Previous experiments show that the $\delta$-Medoids agorithm chooses similar amount of representatives while getting better coverage. + +\begin{table}[H] +\begin{tabular}{l|lllll} +\hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +0 & 6000 & 859 & 23 & 23 \\ +1 & 6000 & 176 & 16 & 16 \\ +2 & 6000 & 845 & 20 & 20 \\ +3 & 6000 & 616 & 22 & 22 \\ +4 & 6000 & 630 & 25 & 25 \\ +5 & 6000 & 1622 & 14 & 14 \\ +6 & 6000 & 1181 & 18 & 18 \\ +7 & 6000 & 266 & 19 & 19 \\ +8 & 6000 & 1971 & 16 & 16 \\ +9 & 6000 & 686 & 28 & 28 \\ \hline + +\end{tabular} +\caption{Number of selected representatives for MNIST Fashion dataset} +\end{table} + \begin{figure}[H] \includegraphics[width=\linewidth]{img/exp3_mnist_fashion.png} \caption{Confusion matrices for real network data} diff --git a/text/img/exp5.png b/text/img/exp5.png new file mode 100644 index 0000000000000000000000000000000000000000..4dff07ba38ab2eb359cd266e49a756bd667225ee GIT binary patch literal 56593 zcmb5Vby!qk*FH+8lyoB~h;%myNDT-`cZW1c4Ba8Ef^-T)2t)VKHA;&(bW6z4-TXH1 z_r2HeT<4E-T}Q{*GsE6{Jm_HEs>XAPlbmxSyV0)2lVr99d@skk&2?6`D9QL0h7wsD_ zp+ezIIRAa=Nb`MNM3E)N`0wip^Caef|Bq1^$HZ*m;Z9-d5DE>dLed|B284*Ndi-}n zP&swC9rSEbcSV#4jkFOl&M?x1)4Q5rFp2pL#QDo#yddm^Xp)c68$SE1`@2P$nXDk% z(@=9lxkX?xx84$*ekpWiinST{VC$=5RzjF{| z>L;kYD^;sD$(!;OM9-khI#mn&3W8vV_`HNH>45&!Hwo#393WHfkSWEL5X5z>UhZs~ 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18:44:20 +0200 Subject: [PATCH 57/65] VH comment incorporated --- text/hlavac-thesis.bib | 20 +++ text/hlavac-thesis.tex | 220 ++++++++++++++++-------------- text/img/complexity.png | Bin 8778 -> 23296 bytes text/img/delta_medoids_select.png | Bin 46596 -> 63321 bytes 4 files changed, 137 insertions(+), 103 deletions(-) diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib index 8fd4373..9357c53 100644 --- a/text/hlavac-thesis.bib +++ b/text/hlavac-thesis.bib @@ -92,6 +92,15 @@ @misc{dua1998pendigit url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } +@online{xiao2017fashion, + author = {Han Xiao and Kashif Rasul and Roland Vollgraf}, + title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms}, + date = {2017-08-28}, + year = {2017}, + eprintclass = {cs.LG}, + eprinttype = {arXiv}, + eprint = {cs.LG/1708.07747}, +} @techreport{rfc2722, AUTHOR = "Brownlee, N. and Mills, C. and Ruth, G.", TITLE = "{Traffic Flow Measurement: Architecture}", @@ -105,3 +114,14 @@ @techreport{rfc2722 INSTITUTION = "{RFC Editor}", URL={https://www.rfc-editor.org/rfc/rfc2722.txt} } +@article{scikit-learn, + title={Scikit-learn: Machine Learning in {P}ython}, + author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. + and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, + journal={Journal of Machine Learning Research}, + volume={12}, + pages={2825--2830}, + year={2011} +} diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index a57789b..7b78ad7 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -148,7 +148,7 @@ \chapter{Intrusion Detection System}\label{ch:ids} This chapter presents the ideas from network security needed to understand the main objective of this thesis. Different approaches to network intrusion detection systems (IDS) are explained in Section~\ref{sec:ids}. -In Section~\ref{sec:nbad}, the scope is narrowed to network anomaly detection IDS and Section~\ref{sec:challenges}delves deeper into the challenges of it. +In Section~\ref{sec:nbad}, the scope is narrowed to network anomaly detection IDS and Section~\ref{sec:challenges} delves deeper into the challenges of it. Section~\ref{sec:ctadf} serves as an introduction to Cognitive Targeted Anomaly Detection Framework used by Cisco. Methods researched in this thesis were tested on real data collected from Cognitive Targeted Anomaly Detection Framework. @@ -158,14 +158,14 @@ \section{Types of Intrusion Detection Systems}\label{sec:ids} IDS that is explicitly designed for monitoring computer network is called Network IDS (NIDS) as in comparison to host IDS and hybrid IDS. Host IDS focuses on monitoring the internal state of a computer system (e. g. mainframe computer) and its dynamic behavior. Hybrid IDS combines different techniques including the ones used in a host and network IDS creating an IDS that can leverage information from each of its parts for better intrusion detection. -Examples of NIDS software are \cite{cooper2019bestids} : +Examples of NIDS software are \cite{cooper2019bestids}: \begin{itemize} \item Snort \item Suricata \item Bro Network Security Monitor \end{itemize} -These systems are used on computer networks to detect and sometimes even prevent attacks that are threats to one of the essential services of such a network. -These basic services are\cite{mukherjee1994network}: +These systems are used on computer networks to detect and or even prevent attacks that are threats to the essential services of such a network. +These basic services are \cite{mukherjee1994network}: \begin{itemize} \item Data confidentiality \item Data and communication integrity @@ -176,7 +176,7 @@ \section{Types of Intrusion Detection Systems}\label{sec:ids} As each kind of intrusion is better detected by a different method, there are several types of IDS \cite{liao2013intrusion}: \begin{itemize} \item \textbf{Signature-based} (knowledge-based): Patterns of known attacks or threats are being compared to captured events for intrusion detection. - \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time, and current network behavior is compared to expected (model) behavior for anomalies. + \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time, and the current network behavior is compared to expected (model) behavior for anomalies. \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. \end{itemize} Cognitive Targeted Anomaly Detection Framework combines all of these approaches. @@ -209,7 +209,7 @@ \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} \todo{ \todo{Explain better, what is the goal of anomaly detection, Grill Thesis - toto ma ted malou prioritu} That is why models are calculated for whole networks where it is possible to create a model that reflects the behavior of the network. -This method is widely used, and on smaller (several devices) networks it is very efficient. +This method is widely used, and very efficient on smaller (several devices) networks. However, capturing flow data from a big network of thousands of devices can result in too much information. One can easily miss one infected device generating different traffic. The noise in the collected data is too high. @@ -217,7 +217,7 @@ \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} \todo{ To give an example, imagine detection of a single infected device, such as a printer in a 10 story office building. This printer, being a part of a botnet, was ordered to generate traffic for DDoS (Distributed Denial of Service) attacks. Looking at the traffic of the printer before and after infection it could easily be seen that it increased by several hundred or even thousands of percent. -But an anomaly detection system could hardly notice a change when considering the traffic of the whole office building. +However, an anomaly detection system could hardly notice a change when considering the traffic of the whole office building. If an AD system does not mark malicious traffic as an anomaly, it is considered a false negative. That is another challenge AD systems are struggling with, to keep the number of missed attacks at 0 or as close to it as possible. @@ -258,7 +258,7 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} \end{itemize} The frequency is defined as: \begin{equation} -F_s^h = \frac{i}{n}\sum\limits_{1}{n}I(t_i, s, h)\:, \tag{1} +F_s^h = \frac{i}{n}\sum\limits_{1}^{n}I(t_i, s, h)\:, \tag{1} \end{equation} where $n$ is the number of time windows, $I$ is the indicator function, which is 1 if the network host $h$ visited the server $s$ in the timewindow $t_i$ and 0 otherwise. This ratio-based frequency ensures that frequently visited servers (e.g., Google, Facebook) do not overshadow the less frequently visited servers. @@ -272,7 +272,7 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: \begin{equation} -\textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:, \tag{2} +\textrm{sim}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:, \tag{2} \end{equation} where $F^a$, $F^b$ represent the frequency and $S$ represents the union of sets of servers visited by the network hosts $a$ and $b$. @@ -330,7 +330,7 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Therefore there is a constraint added for clustering methods. It can be either the maximum number of clusters or the maximum distance between two clusters. -A straightforward example of clustering is a method called agglomerative hierarchical clustering. +A straightforward example of clustering is a method called agglomerative hierarchical clustering \cite{guttag2016introduction}. Given $N$ objects in a dataset, it creates $N$ clusters - meaning there is a cluster for each object. The method looks for two closest clusters and merges them into one. This agglomerative merging continues until the constraint is met, meaning until there is a certain number of clusters or until the distance between closest clusters exceeds a certain threshold. @@ -338,16 +338,16 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Also, the algorithm has a time complexity of $\mathcal{O}(n^2)$. Therefore, it cannot be used in big datasets. -An example of a much faster clustering algorithm that is also greedy is $K$-means. +An example of a much faster clustering algorithm that is also greedy is $K$-means \cite{guttag2016introduction}. The '$K$' in $K$-means stands for the number of clusters that we want to get as a result. -To use this algorithm the number of desired clusters has to be known in advance. +To use this algorithm, the number of desired clusters has to be known in advance. $K$-means randomly chooses $K$ centroids in the space of the dataset and then assigns each point to a centroid. After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. The algorithm stops when the centroids of clusters stop changing. -Algorithm~\ref{alg:kmeans} shows the pseudocode of $K$-means. +Algorithm~\ref{alg:kmeans} shows the pseudocode of $K$-means algorithm. \begin{algorithm}[t]\label{alg:kmeans} - \caption{K-Means} + \caption{$K$-means} \label{k_mean_pseudocode} \begin{algorithmic}[1] \INPUT data $X = x_0, x_1, ..., x_n$; number of clusters $k$ @@ -380,22 +380,23 @@ \section{Introduction to Clustering}\label{sec:clust_intro} It is the most common clustering algorithm as it typically converges in a few iterations. For more details about basic clustering algorithms see \cite{guttag2016introduction}. -The clustering as explained in this section has restrictions that make it make it inaplicable in Cognitive Targeted Anomaly Detection Framework. -Firstly the number of clusters is not previously known, so choosing $K$ for $K$-means is not an option, and secondly, the distance measure used does not form a metric space. +The clustering, as explained in this section has restrictions make it inaplicable in Cognitive Targeted Anomaly Detection Framework. +Firstly, the number of clusters is not previously known, so choosing $K$ for $K$-means is not an option, and secondly, the distance measure used does not form a metric space. Next section delves deeper into what it means when a measure does not form a metric space. \section{Clustering in Topological Space}\label{sec:cluster_topo} -This section explains the difference between metric and topological space and clustering approaches for topological spaces. +This section explains the difference between metric and topological spaces and clustering approaches for topological spaces. -A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X \to \mathbb{R}$ which satisfies the following conditions: +A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping $X \times X \to \mathbb{R}$ which satisfies the following conditions: \begin{enumerate} \item [(i)] $d(x, y) \geq 0$; \item [(ii)] $d(x, y) = 0 \iff x = y$; \item [(iii)] $d(x, y) = d(y, x)$ \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \mathrm{for} x, y, z \in X$. \end{enumerate} +Any function $d$ following these conditions is called distance. \todo{is this realy true?} -The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctadf} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms a subspace for comparing each pair of samples. +The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctadf} does not fulfill the last point of the definition above. Consequently, it is not a distance and does not form a metric space. Furthermore, it is a pairwise similarity that forms a subspace for comparing each pair of samples. This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: @@ -409,20 +410,20 @@ \section{Clustering in Topological Space}\label{sec:cluster_topo} \todo{explain about neighborhoods??} -K-Means clustering from the previous section cannot be used in topological space as the centroids do not exist. -In many cases, there is no prior knowledge of k, as is the case in this thesis. +$K$-means clustering from the previous section cannot be used in topological space as the centroids do not exist. +In many cases, there is no prior knowledge of the desired number of clusters $K$, as is the case in this thesis. A popular method that is used for clustering in non-metric spaces is called community-based clustering. -Community-based clustering detects communities in the data. +Community-based clustering detects communities in the data \cite{kopp2018community}. A community is a subset of examples in the data, that is densely connected with each other. One of the ways how to detect communities in a graph is to create a full-adjacency matrix. This matrix contains all connections between all nodes in the given graph. Analyzing the matrix then can tell us about the densities in different parts of graphs. -Louvain method is a similar approach to clustering when we do not have a simple graph but a set of samples and a pairwise similarity measure. +Louvain method is a similar approach to clustering when we do not have a simple graph but a set of samples and a pairwise similarity measure \cite{kopp2018community}. This method relies on creating a full similarity matrix for the whole dataset and then looking for communities in the data. -In Cognitive Targeted Anomaly Detection Framework Louvain method cannot be used directly as calculating the full similarity matrix has a complexity of $\mathcal(n^2)$, which is impossible to calculate for a large network. +In Cognitive Targeted Anomaly Detection Framework, Louvain method cannot be used directly as calculating the full similarity matrix has a complexity of $\mathcal{O}(n^2)$, which is impossible to calculate for a large network. That is why an approximative clustering method is used. This method iteratively samples network hosts and runs the clustering algorithm on the sampled hosts. Each iteration creates or updates cluster prototypes. @@ -452,7 +453,7 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ Representative selection aims to find a minimal subset of examples from a cluster, that carries sufficient information about the whole cluster. This problem was well defined in \cite{liebman2015representative} and the following definition is taken from that paper. -Let $X$ be a data set, $d : X \times X \to \mathbb{R} +$ be a distance measure (not necessarily a metric), and $\delta$ be a distance threshold below which samples are considered sufficiently similar. +Let $X$ be a data set, $d \colon X \times X \to \mathbb{R} +$ be a distance measure (not necessarily a metric), and $\delta$ be a distance threshold below which samples are considered sufficiently similar. The task is finding a representative subset $Z \subseteq S$ that best encapsulates the data. Two following requirements are imposed on an algorithm for finding a representative subset: \begin{itemize} @@ -477,7 +478,7 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ This definition of representative selection problem serves well for this paper. \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_metric} -A metric dataset has to have the same number of features for each sample and a metric that fulfills all the requisites in the definition of metric space. +A metric dataset has to have the same number of features for each sample and a metric that fulfills all the requisites in the definition of the metric space. Selecting a prototype from this dataset is often best achieved by calculating a centroid for a given cluster. A centroid can be calculated as the mean of the points in the cluster. An example of a centroid in a cluster can be found in Figure~\ref{img:centroids} a). @@ -495,9 +496,9 @@ \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_met Formally, medoid is defined as: \begin{equation} x_{medoid} = \mathrm{argmin}_{y \in \{x_1, x_2, ..., x_n\}} \sum_{i=1}^{n}{d(y, x_i)}\:, \tag{5} -\end{equation} +\end{equation} \todo{zavest matematicky symbol pokud chci pouzitlimits} where $x_1, x_2, ..., x_n$ is a set of $n$ points in a space with a distance function $d$. -In Figure~\ref{img:medoids} medoids were chosen instead of centroids. +In Figure~\ref{img:medoids}, medoids were chosen instead of centroids. \begin{figure} \includegraphics[width=\linewidth]{img/medoids.png} \caption{Choice of medoids} @@ -508,13 +509,13 @@ \subsection{Representative Selection in a Topological Space}\label{sec:rep_selec Clearly, the concept of centroid explained in the previous section cannot be used to solve the problem of finding a representative in an arbitrary space (i.e., topological space). Medoids, of course, can be selected for datasets in arbitrary spaces. -$K$-Medoids algorithm resembles $K$-Means algorithm in breaking the dataset into $K$ groups. +$K$-Medoids algorithm resembles $K$-means algorithm in breaking the dataset into $K$ groups. In these groups it finds a medoid that is minimizes the distance to each other point in the group. $K$-Medoids is most commonly used for representative selection in topological spaces. There are not many more well-explored methods for solving this problem. The one that is used in this thesis is explained in \cite{liebman2015representative}. -They took the main ideas of $K$-Means clustering and transformed them for usage in a non-metric space with a pair-wise similarity measure. +They took the main ideas of $K$-means clustering and transformed them for usage in a non-metric space with a pair-wise similarity measure. Instead of stating the $K$ in advance they state a parameter $ 0 \leq \delta \leq 1$ that serves as a constraint. Then they separate the cluster into subclusters based on this parameter. Each of these subclusters is represented by a medoid. @@ -562,7 +563,8 @@ \subsection{$\delta$-Medoids One-Shot} The idea behind this algorithm is to go through the dataset looking for representatives that differ at least by the given parameter $\delta$ from each previously selected representatives. By taking this approach, it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. -The algorithm is shown in Algorithm~\ref{alg:delta_medoids_one_shot}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. +The algorithm is shown in Algorithm~\ref{alg:delta_medoids_one_shot}. +It have been optimized for better memory efficiency as opposed to the version in the cited paper. The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. \begin{algorithm} @@ -596,9 +598,10 @@ \subsection{$\delta$-Medoids Full} If two consecutive passes through the data do not change any medoid, the algorithm stops. This algorithm is shown in Algorithm~\ref{alg:delta_medoids_full}. Original algorithm from \cite{liebman2015representative} lacks the routine \textit{ReduceClusters} on line 24. -This routine is introduced in this thesis as an improvement of the algorithm because of the algorithm properties found in Section~\ref{sec:exp1}. +This routine is introduced in this thesis as an improvement of the algorithm because the original algorithm selected too many representatives. +It is explained in greater detail in the Section~\ref{sec:modified} -This approach is slower, as the main routine from one shot algorithm has to be run multiple times. +This approach is slower than $\delta$-Medoids, as the main routine from one shot algorithm has to be run multiple times. Experimental results of \cite{liebman2015representative} show that the algorithm converges in very few ($<$10) cycles. \todo{To samozrejme zalezi na volbe datasetu, jak to jednoduse vysvetlit.} On the other hand, it is able to select better medoids, thus getting rid of the difficult choice of the first representative to select. @@ -640,13 +643,17 @@ \subsection{$\delta$-Medoids Full} \end{algorithmic} \end{algorithm} -The subroutine \textit{ReduceClusters} shown in Algorithm~\ref{alg:reduce} helps to reduce the number of selected representatives. -If a representative does not cover at least 1\% of the cluster, the routine tries to add him to the coverage of the representative that is the most similar. -If no such similar representative exists, it is considered noise and the representative is dropped from the cluster. - \subsection{$\delta$-Medoids Modified} +\label{sec:modified} -\todo{modified algorithm betterexplained} +The algorithm $\delta$-Medoids Full does not have any restriction for the number of representatives it selects for each cluster. +Results in Chapter~\ref{ch:experiments} show that the full algorithm can select up to a third of a big complex cluster which does not satisfy the Criterion 1 from Section~\ref{sec:def_rep_selection} properly. +To reduce the number of representatives a subroutine \textit{ReduceClusters} was introduced as a modification to the original algorithm. +The subroutine is shown \textit{ReduceClusters} shown in Algorithm~\ref{alg:reduce}. + +The basic idea behind this modification is to get rid of representatives that cover a small portion of the whole dataset by either merging them with similar representatives or dropping them entirely. +If a representative does not cover at least 1\% of the cluster, the routine tries to add it to the coverage of the representative that is the most similar. +If no such similar representative exists, it is considered noise and the representative is dropped from the cluster. \begin{algorithm} \caption{ReduceClusters} @@ -671,7 +678,8 @@ \subsection{$\delta$-Medoids Modified} \end{algorithmic} \end{algorithm} -The time complexity is of $\delta$-Medoids is shown on Figure~\ref{img:complexity}. +Figure~\ref{img:complexity} shows that $\delta$-Medoids Modified is a bit faster than the version full version. +This combined with the fact that it selects lower number of representatives shows that the modification improved its performance for this thesis's use case. \begin{figure} \includegraphics[width=\linewidth]{img/complexity.png} @@ -687,12 +695,13 @@ \chapter{Datasets}\label{ch:datasets} That is why, before moving to a dataset collected from the real network, the selected algorithms were first tested on artificially created datasets and well labeled datasets found in the literature. Below, the datasets used in this thesis are presented. -There are three types of datasets used: artificially created, image recognition and real network dataset. -Each section corresponds to one of the listed types. +Three types of datasets were used: artificially created, image recognition and real network security dataset. +Each section corresponds to one of the listed dataset types. \section{Datasets Created for this Work} These datasets were created to test the specific properties of tested methods. +They were made using the Scikit-Learn library for Python 3 \cite{scikit-learn}. The datasets created are the following: \begin{itemize} \item Blobs 3D @@ -707,12 +716,11 @@ \section{Datasets Created for this Work} \caption{Graphs of all datasets} \label{img:datasets} \end{figure} -Similarity measure used in these datasets was Euclidean distance. The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter~\ref{ch:theory}. The Overlap dataset consists of 3 clusters in shapes of blocks. -These blocks partly overlap to test how selected algorithms cope with overlapping clusters. -Circles 3D and Moons are one of the hardest datasets to represent even by methods that are designed specifically for metric space. +These blocks overlap partly to test how selected algorithms cope with overlapping clusters. +Circles 3D and Moons are one of the hardest datasets to represent even by methods that are designed specifically for the metric space. These two datasets were chosen to test the resistance to noise in selecting representatives. All of these datasets are shown in Figure~\ref{img:datasets}. @@ -728,7 +736,7 @@ \section{Image Recognition Datasets} Features represent coordinate information about the digits as they were written on a $500\times500$ pixel frame. This dataset is split to train dataset (70\%) and test dataset (30\%). -The MNIST Fashion dataset \todo{citation} consist of a training set of 60,000 samples and 10,000 test samples. +The MNIST Fashion dataset \cite{xiao2017fashion} consist of a training set of 60,000 samples and 10,000 test samples. A sample from this dataset is a black and hite grayscale image of a piece of clothing or a shoe. Each sample belongs to one of 10 classes. This problem is much more complex than handwritten digits and is currently used as a benchmark dataset for classification problems. @@ -739,7 +747,8 @@ \section{Network Security Dataset}\label{sec:real-data} Proxy logs were used to capture the connections. Hosts that appeared in that capture were clustered using the method explained in Section~\ref{sec:cluster_topo}. These clusters with all hosts labeled were used as real network dataset in this thesis. - +Cluster sizes range from less than a hundred to several thousand of network hosts in a dataset. +\todo{co jeste napsat o datasetu - nebo toto staci?} %========================================CHAPTER================================== @@ -751,15 +760,12 @@ \chapter{Experiments}\label{ch:experiments} All experiments were made in Jupyter Notebook technology using Python 3.7 kernel. For loading data from files and storing them in memory the Pandas library version 0.23.4 was used.\todo{link to documentation}. -%For all experiments below the - \section{Visualizable datasets}\label{sec:exp1} The first experiment was run on the data specifically created for this thesis. -The methods that were explained in the previous chapter are designed to work in non-metric space but they still work in a metric space. -Using them on metric data and visualizing the results helped a lot to check the assumptions about results experimentally. -Alsi the ability to select theminimal number of representatives was selected. +The methods that were explained in Section~\ref{sec:relevant_methods} are designed to work in non-metric space but they still work in a metric space. +Using them on visualisable benchmark datasets with known problems helped to determine their properties. \subsection{Experimental Setup} Datasets used for this experiment were: @@ -772,6 +778,7 @@ \subsection{Experimental Setup} All 5 algorithms were run. The $\delta$ was estimated as 5\% quantile of distances from one random point in the data to all of the others. +The similarity measure used in these datasets was Euclidean distance. \subsection{Results} @@ -783,9 +790,9 @@ \subsection{Results} \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 400 & 21 & 23 & 23 & 23 & 23 \\ -B & 400 & 17 & 20 & 20 & 20 & 20 \\ -C & 400 & 19 & 20 & 20 & 20 & 20 \\ \hline +A & 400 & 21 & 23 & 25 & 16 & 16 \\ +B & 400 & 17 & 20 & 23 & 20 & 20 \\ +C & 400 & 19 & 20 & 22 & 20 & 20 \\ \hline \end{tabular} \caption{Number of selected representatives for Blobs 3D dataset} \end{table} @@ -852,32 +859,35 @@ \subsection{Results} \end{figure} -\subsection{Conclusion} -At first glance, all the confusion matrices look almost perfect and as expected. -The number of representatives selected did not exceed 30 in any cluster. -The number of missed classification is low even in overlapping or complex datasets. -However, when the results were visualized for one cluster, the delta medoids algorithm did not result in expected selection. -As can be seen on Figure~\ref{img:blobs_border_select}, the algorithm picked the samples on the edge of the dataset that did not cover many points in their neighborhoods. -\begin{figure}[H] - \includegraphics[width=\linewidth]{img/delta_medoids_select.png} - \caption{$\delta$-Medoids choosing the border samples of a cluster} - \label{img:blobs_border_select} -\end{figure} -Selecting the border samples clearly was not intended and the $\delta$-Medoids algorithm had to select only the relevant samples. -The relevance of a representative was determined to the number of points that fit into its $\delta$-neighborhood. \todo{where to list the changes, here or in the theoretical chapter, theory better I think} +\subsection{Conclusion for Visualizable Datasets} +For the Blobs dataset, which represent an easy representation problem, all algorithms were able to represent clusters perfectly. +The number of representatives selected is higher because of the choice of $\delta$. +This does is not a problem as the number is still relatively small (less than 10\% of the dataset). +Also, in the use case of using these algorithms in dynamically changing clusters use case, the higher number of representatives is requested to keep track of the cluster over time. -The results of this experiment empirically supported the expected result of not selecting the border samples from a dataset as can be seen on Figure~\ref{img:blobs_good_select} -\begin{figure}[H] - \includegraphics[width=\linewidth]{img/delta_medoids_select_better.png} - \caption{$\delta$-Medoids choosing only relevant samples of a cluster} - \label{img:blobs_good_select} +The Overlap dataset represents a harder problem with overlapping clusters the results are still sufficient as for each cluster both precision and recall are above 0.85. + +Both Moons and Circles 3D are harder problems for selecting representatives. +This experiment has shown that with a well chosen $\delta$ representatives can be chosen with each of the algorithm to form a decent cluster prototype. +On these two datasets the Random Selection algorithm clearly peforming worse. \todo{clearly from confusion matrices, how to write this} + +From the results listed the difference between $\delta$-Medoids Full and Modified is not easily distinguishable. +The difference between these two algorithms can be seen on Figure~\ref{img:difference_modified}. +The full version of the algorithm tends to select the border samples of dataset as representatives. +In these datasets the results do not show this because they are relatively dense and have normal distribution. +On datasets that are more sparse this leads to a high number of representatives selected. + +\begin{figure}[t] + \includegraphics[width=\linewidth]{img/delta_medoids_select.png} + \caption{Difference between $\delta$-Medoids Full and Modified} + \label{img:difference_modified} \end{figure} -This sanity check by visualization fulfilled all of the expectation. \section{Image Recognition Datasets}\label{sec:exp3} After the algorithms were tested on visualizable data, the experiments with well known labeled datasets could start. The motivation for this experiment was to test the algorithm results on well explored and well-annotated datasets commonly used as a benchmark for clustering. +In this and the following experiments only the $\delta$-Medoids Modified was tested. \todo{rerun with delta medoids full or better explain why it was ruled out} \subsection{Experimental Setup} Datasets used for this experiment were: @@ -886,7 +896,8 @@ \subsection{Experimental Setup} \item MNIST Fashion \end{itemize} -The $\delta$ was estimated as \todo{how is median at lower ten percent called, put it to maths} times 1.05 from distances from one point in the data to all of the others. +The $\delta$ was estimated as 5\% quantile of distances from one random point in the data to all of the others. +The similarity measure used in these datasets was Euclidean distance. \subsection{Results} @@ -897,11 +908,11 @@ \subsection{Results} \begin{table}[H] \begin{tabular}{l|lllll} \hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline 0 & 780 & 33 & 33 & 33 & 33 \\ 1 & 779 & 33 & 41 & 41 & 41 \\ 2 & 780 & 17 & 15 & 15 & 15 \\ -3 & 719 & 12 & 9 & 9 & 9 \\ +3 & 719 & 12 & 9 & 9 & 9 \\ 4 & 780 & 25 & 24 & 24 & 24 \\ 5 & 720 & 25 & 27 & 27 & 27 \\ 6 & 720 & 16 & 16 & 16 & 16 \\ @@ -926,7 +937,7 @@ \subsection{Results} \begin{table}[H] \begin{tabular}{l|lllll} \hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline 0 & 6000 & 859 & 23 & 23 \\ 1 & 6000 & 176 & 16 & 16 \\ 2 & 6000 & 845 & 20 & 20 \\ @@ -948,13 +959,14 @@ \subsection{Results} \label{img:exp4} \end{figure} \todo{table with representatives selected} -\subsection{Conclusion} -Using algorithms on real datasets started to show more significant differences between results in algorithms. -An interesting thing is that Greedy Selection and $\delta$-Medoids One-Shot select similar number of representatives and their results are also very similar. -This is interesting as the complexity of these algorithms differs a lot, Greedy Selection being much slower with $\mathcal(n^2)$. \todo{better wording of this conclusion} - -\section{Experiment 4}\label{sec:exp4} +\subsection{Conclusion for Image Recognition Datasets} +Using algorithms on real datasets shows more significant differences between results in algorithms. +Especially the difference in numbers of representatives chosen for different methods. +The $\delta$-Medoids Modified choses the lowest number of representatives while getting the best results. +Greedy Selection and $\delta$-Medoids One-Shot select similar number of representatives and their results are also very similar. +This is interesting as the complexity of these algorithms differs a lot, Greedy Selection being much slower with $\mathcal(n^2)$. +\section{Network Security Data}\label{sec:exp4} \subsection{Experimental Setup} For this experiment clustered hosts from a real network were used. @@ -972,23 +984,23 @@ \subsection{Results} \begin{table}[H] \begin{tabular}{l|lllll} \hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -1024 & 72 & 0 & 8 & 8 & 8 \\ -865 & 89 & 0 & 28 & 28 & 28 \\ -323 & 2125 & 0 & 821 & 40 & 40 \\ -68 & 116 & 0 & 82 & 36 & 36 \\ -645 & 83 & 0 & 56 & 56 & 56 \\ -1004 & 46 & 0 & 38 & 38 & 38 \\ -43 & 55 & 0 & 31 & 31 & 31 \\ -1196 & 835 & 0 & 441 & 34 & 34 \\ -253 & 59 & 0 & 47 & 47 & 47 \\ -112 & 77 & 0 & 66 & 66 & 66 \\ -946 & 2065 & 0 & 781 & 30 & 30 \\ -116 & 238 & 0 & 71 & 17 & 17 \\ -393 & 51 & 0 & 38 & 38 & 38 \\ -756 & 127 & 0 & 92 & 34 & 34 \\ -124 & 67 & 0 & 48 & 48 & 48 \\ -221 & 50 & 0 & 50 & 50 & 50 \\ \hline + \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 72 & 8 & 8 & 8 & 8 \\ +B & 89 & 28 & 28 & 28 & 28 \\ +C & 2125 & 821 & 821 & 40 & 40 \\ +D & 116 & 83 & 82 & 36 & 36 \\ +E & 83 & 57 & 56 & 56 & 56 \\ +F & 46 & 38 & 38 & 38 & 38 \\ +G & 55 & 32 & 31 & 31 & 31 \\ +H & 835 & 460 & 441 & 34 & 34 \\ +I & 59 & 47 & 47 & 47 & 47 \\ +J & 77 & 66 & 66 & 66 & 66 \\ +K & 2065 & 846 & 788 & 30 & 30 \\ +L & 238 & 78 & 71 & 17 & 17 \\ +M & 51 & 38 & 38 & 38 & 38 \\ +N & 127 & 91 & 91 & 34 & 34 \\ +O & 67 & 48 & 48 & 48 & 48 \\ +P & 50 & 50 & 50 & 50 & 50 \\ \hline \end{tabular} \caption{Number of selected representatives for real network data} \end{table} @@ -1000,10 +1012,9 @@ \subsection{Results} \label{img:exp4} \end{figure} -\todo{here add better cluster names for sure} \todo{get precision and recall values per cluster} -\subsection{Conclusion} +\subsection{Conclusion for Network Security Data} The results show that the $\delta$-Medoids algorithm is significantly better than selecting random representatives from the cluster. Even for clusters with several thousand of samples few samples were selected a while retaining good properties for assigning points to clusters. The downside is the speed of the algorithm but this was not a part of this experiment. @@ -1062,7 +1073,10 @@ \chapter{Abreviations} % \printglossaries \begin{description} %VH I changed the order of NBAD and NIDS to keep alphabetical order - \item[IDS] Intrusion Detection System + \item[AD] Anomaly Detection + \item[DDoS] Distributed Denial of Service + \item[DPI] Deep Packet Inspection + \item[IDS] Intrusion Detection System \item[NBAD] Network-Based Anomaly Detection \item[NIDS] Intrusion Detection System \end{description} diff --git a/text/img/complexity.png b/text/img/complexity.png index aa53fbfa87b298b7fbdbca6e7f810d971256b3f5..c7b11f892d3bd47b8802d9e72a73825f2f3c3c6c 100644 GIT binary patch literal 23296 zcmd>mby(E$sC_~o}L#YTzOLwSSQ9#h41Sx3-h8~c{0?CmY zq*1!N8My0|^r?uU2Y;*S)-kG=LcFd7HpKaYbCUcl=^k7RZ1QK%y)kgr|MJzkserl^CAwu8Ej ziG%Y!J7bj9J%>k@HV&3%_y2SSiwz{X+6>}FP?GaHzJ;b(W6jBTx-QUzg%0pp|ThG?dl(NyO5v9_R1hXeD+*F zj{NMu_Fo_FpPATQE9c_!;gz4?yyKajOHRgmZ>`<4uxf~3EPMFmzJ^9ffpyRL#AW1Q 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z263cZq5y{|*_g%+*^ljNm*O}}TV|SwBTM4EZ?eGNLDDFbWo6@n?J@c}a24VzqqBe( z6F3-;WSrJ9hXq~!XfPs*1_dfW+u65_7Rl873-od#p-_<(fTzu0rcdMtWq=od*UsON j2n^kykm!GEDp?5dbAO*?ePn|^R)F631_v_^kgon0QWdQt From 5122dd65b73f67276f4eeb667db13086339a8158 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 12 May 2019 19:53:48 +0200 Subject: [PATCH 58/65] added fit experiment --- text/hlavac-thesis.tex | 6 +++--- text/img/exp5.png | Bin 56593 -> 33597 bytes 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 7b78ad7..c40869b 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -1021,13 +1021,12 @@ \subsection{Conclusion for Network Security Data} \todo{expand the conclusion} -\section{Experiment 5}\label{sec:exp5} +\section{Average Fit of Data}\label{sec:exp5} In this experiment the average data fit as defined in Section~\ref{sec:representative_selection} was tested. \subsection{Experimental Setup} -In this experiment one cluster was represented by $\delta$-Medoids and Random Selection algorithms. -The cluster was selected from dataset\todo{dataset, multiple clusters?}. +In this experiment one complex cluster (cluster 'C') from Network Security Dataset was represented by $\delta$-Medoids and Random Selection algorithms. After the representatives were selected, the percentage of coverage by each representative was calculated and plotted into graph. @@ -1044,6 +1043,7 @@ \subsection{Conclusion} In the random selection, there is the possibility of selecting either the border cases or two points with overlapping neighborhoods. 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z;XYrD>-^3*jy9og%as@KV1V*YFZ!uNEdKkg00PH`25qGy01m_pHq-eguG|ykgABm_ zAZiK-1avBQTc`=I*PlX+o#N&#)boGARuE6BHr*!vl51dH_PuF85g;}HoW{9-k#<|6 z1Tmdp%R)8&UtP~XqlP+SKi@lNj$Mfb<-%P5nS|KjQ#GP^xNhT}6XhG&@kh_a9N8dBg=5_TcubYIpo2>b4dC jn^^z<5(*-KfA6|Rj?}vse&rOK4uH)R1V_bZoFo4S&VNeC From f545f6d3fa6ca97654edb6101c0e6a3e1e8e0bb2 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 12 May 2019 21:29:49 +0200 Subject: [PATCH 59/65] added conclusion of whole thesis --- text/hlavac-thesis.bib | 10 ++++++ text/hlavac-thesis.tex | 72 ++++++++++++++++++++++-------------------- 2 files changed, 47 insertions(+), 35 deletions(-) diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib index 9357c53..08ffcd6 100644 --- a/text/hlavac-thesis.bib +++ b/text/hlavac-thesis.bib @@ -125,3 +125,13 @@ @article{scikit-learn pages={2825--2830}, year={2011} } +@inproceedings{mckinney2010data, + title={Data structures for statistical computing in python}, + author={McKinney, Wes and others}, + booktitle={Proceedings of the 9th Python in Science Conference}, + volume={445}, + pages={51--56}, + year={2010}, + organization={Austin, TX} +} + diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index c40869b..171c78b 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -399,7 +399,7 @@ \section{Clustering in Topological Space}\label{sec:cluster_topo} The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctadf} does not fulfill the last point of the definition above. Consequently, it is not a distance and does not form a metric space. Furthermore, it is a pairwise similarity that forms a subspace for comparing each pair of samples. This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. -Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: +Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$, and with the following properties: \begin{enumerate} \item [(i)] The empty set $\emptyset$ is in $F$. \item [(ii)] The given set $S$ is in $F$. @@ -541,7 +541,6 @@ \subsection{Random Selection}\label{subsec:random_select} It, of course, depends on each network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. \subsection{Greedy Selection}\label{subsec:greedy_select} -\todo{obrazky sem a do dalsi sekce} The greedy selection algorithm is a straightforward algorithm that selects a random sample from the set as the first representative. Then it looks for the most different sample in the rest of the dataset. Once the desired sample is found, the algorithm removes all neighboring samples with at least the similarity of $\delta$ from the set. @@ -758,7 +757,7 @@ \chapter{Experiments}\label{ch:experiments} For each experiment, the motivation, experimental setup, results and conclusion are listed. All experiments were made in Jupyter Notebook technology using Python 3.7 kernel. -For loading data from files and storing them in memory the Pandas library version 0.23.4 was used.\todo{link to documentation}. +For loading data from files and storing them in memory the Pandas library version 0.23.4 was used \cite{mckinney2010data}. \section{Visualizable datasets}\label{sec:exp1} @@ -783,10 +782,11 @@ \subsection{Experimental Setup} \subsection{Results} The results for each dataset are listed below. +\todo{make references to each table and picture or put hard placement for them?} \textbf{Blobs 3D} -\begin{table}[H] +\begin{table}[t] \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -797,7 +797,7 @@ \subsection{Results} \caption{Number of selected representatives for Blobs 3D dataset} \end{table} -\begin{figure}[H] +\begin{figure}[t] \includegraphics[width=\linewidth]{img/exp1_blobs.png} \caption{Confusion matrices for Blobs 3D dataset} \label{img:exp1_blobs} @@ -805,7 +805,7 @@ \subsection{Results} \textbf{Overlap} -\begin{table}[H] +\begin{table}[t] \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -816,7 +816,7 @@ \subsection{Results} \caption{Number of selected representatives for Overlap dataset} \end{table} -\begin{figure}[H] +\begin{figure}[t] \includegraphics[width=\linewidth]{img/exp1_overlap.png} \caption{Confusion matrices for Overlap dataset} \label{img:exp1_overlap} @@ -824,7 +824,7 @@ \subsection{Results} \textbf{Moons} -\begin{table}[H] +\begin{table}[t] \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -834,7 +834,7 @@ \subsection{Results} \caption{Number of selected representatives for Moons dataset} \end{table} -\begin{figure}[H] +\begin{figure}[t] \includegraphics[width=\linewidth]{img/exp1_moons.png} \caption{Confusion matrices for Moons dataset} \label{img:exp1_moons} @@ -842,7 +842,7 @@ \subsection{Results} \textbf{Circles} -\begin{table}[H] +\begin{table}[t] \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -852,7 +852,7 @@ \subsection{Results} \caption{Number of selected representatives for Circles dataset} \end{table} -\begin{figure}[H] +\begin{figure}[t] \includegraphics[width=\linewidth]{img/exp1_circles.png} \caption{Confusion matrices for Circles dataset} \label{img:exp1_circles} @@ -887,7 +887,7 @@ \section{Image Recognition Datasets}\label{sec:exp3} After the algorithms were tested on visualizable data, the experiments with well known labeled datasets could start. The motivation for this experiment was to test the algorithm results on well explored and well-annotated datasets commonly used as a benchmark for clustering. -In this and the following experiments only the $\delta$-Medoids Modified was tested. \todo{rerun with delta medoids full or better explain why it was ruled out} +In this and the following experiments only the $\delta$-Medoids Modified was tested. \subsection{Experimental Setup} Datasets used for this experiment were: @@ -904,8 +904,8 @@ \subsection{Results} The results for different methods are listed below. \textbf{Pendigits} - -\begin{table}[H] +\todo{make references to each table and picture or put hard placement for them?} +\begin{table}[t] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -923,7 +923,7 @@ \subsection{Results} \caption{Number of selected representatives for Pendigit dataset} \end{table} -\begin{figure}[H] +\begin{figure}[t] \includegraphics[width=\linewidth]{img/exp3_pendigits.png} \caption{Confusion matrices for Pendigits dataset} \label{img:exp3_pendigits} @@ -934,7 +934,7 @@ \subsection{Results} In the experiment for this dataset the Greedy Selection algorithm was omitted as the experiments would take too long on clusters that contain 6000 samples. Previous experiments show that the $\delta$-Medoids agorithm chooses similar amount of representatives while getting better coverage. -\begin{table}[H] +\begin{table}[t] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -953,11 +953,11 @@ \subsection{Results} \caption{Number of selected representatives for MNIST Fashion dataset} \end{table} -\begin{figure}[H] +\begin{figure}[t] \includegraphics[width=\linewidth]{img/exp3_mnist_fashion.png} \caption{Confusion matrices for real network data} \label{img:exp4} -\end{figure} \todo{table with representatives selected} +\end{figure} \subsection{Conclusion for Image Recognition Datasets} Using algorithms on real datasets shows more significant differences between results in algorithms. @@ -981,7 +981,7 @@ \subsection{Results} The results for different methods are listed below. -\begin{table}[H] +\begin{table}[t] \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -1004,16 +1004,13 @@ \subsection{Results} \end{tabular} \caption{Number of selected representatives for real network data} \end{table} -\todo{greedy medoids jeste pocitam} -\begin{figure}[H] +\begin{figure}[t] \includegraphics[width=\linewidth]{img/exp4.png} \caption{Confusion matrices for real network data} \label{img:exp4} \end{figure} -\todo{get precision and recall values per cluster} - \subsection{Conclusion for Network Security Data} The results show that the $\delta$-Medoids algorithm is significantly better than selecting random representatives from the cluster. Even for clusters with several thousand of samples few samples were selected a while retaining good properties for assigning points to clusters. @@ -1032,13 +1029,13 @@ \subsection{Experimental Setup} \subsection{Results} -\begin{figure}[H] +\begin{figure}[t] \includegraphics[width=\linewidth]{img/exp5.png} \caption{\cbl Average fit of representatives in one cluster} \label{img:exp5} \end{figure} -\subsection{Conclusion} +\subsection{Conclusion for Fitting the Data} On average, the $\delta$-Medoids algorithm covers the cluster better with each added representative. In the random selection, there is the possibility of selecting either the border cases or two points with overlapping neighborhoods. This can occur even more often in data, that do not have standard distribution and can be denser in one part of a cluster than the other. @@ -1049,19 +1046,24 @@ \subsection{Conclusion} %========================================CHAPTER================================== \chapter{Conclusion} -Literature in both Network Anomaly Detection field and representative selection was studied. -Approaches and algorithms thus found were combined and implemented in prototype program. -Both general clustering datasets and specific datasets from security field were tested and compared in the prototype. -For testing the selected method datasets were created and gathered from the internet to create a diverse testbed for comparing algorithms. -From the selected algorithms both $\delta$-Medoids One Shot and $\delta$-Medoids outperformed Random selection of representatives, which is the current solution of representatives selection in the framework. -The improvement was at least by 1\% in each dataset, while average improvement was 2\%. \todo{Double check these numbers.} +A method to represent clusters in non-metric space by a small subset of the whole cluster was implemented and tested in this thesis. +This method was adjusted so that it can be incorporated into Cognitive Targeted Anomaly Detection Framework in the future. + +Methods that are relevant for creating cluster prototypes in a non-metric space were studied. +Several methods including Random Selection, Greedy Selection and $\delta$-Medoids algorithms were chosen as relevant. +Implementation of each algorithm was written and tested on artificially created benchmark datasets. +As a result of these tests new algorithm, $\delta$-Medoids Modified - a modified version of the $\delta$-Medoids Full algorithm, was introduced. +All algorithms were then further tested on image recognition datasets Pendigits and MNIST Fashion. +Finally, the methods were fine-tuned and tested on a real Network Security Dataset captured in a medium-sized company. -\todo{Fine-tune the algorithms for the NBAD field and write here about it if it works.} +The experiments that were performed have shown that the $\delta$-Medoids Modified fulfills the requirements the best. +It selects a small number of representatives from each cluster and is more precise than currently used Random Selection algorithm. +The average fit by representatives of the data was also tested. +A cluster prototype created by $\delta$-Medoids algorithm fits clusters significantly better than randomly selected representatives. -If compared by the requirements from the definition of representative selection problem as we approach it, the following can be concluded. -For fast representative selection that does result in more accurate result than selecting samples randomly, $\delta$-Medoids One Shot algorithm should be used. -If the speed is not of great concern or the datastets have lower tens of thousands of samples, full version of $\delta$ medoids yields the best results. +This thesis explored the nature of the clusters collected on the Internet. +\todo{jak zminit possible future work s kNN graphs atd.} \bibliographystyle{iso690} \bibliography{hlavac-thesis.bib} From ca44956fd7fed48c27a4b5ef451101d76339ad0d Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Mon, 13 May 2019 22:04:19 +0200 Subject: [PATCH 60/65] conlusion updated, abstract, most of todo done --- text/hlavac-thesis.bib | 5 + text/hlavac-thesis.tex | 232 +++++++++++++++++++------------- text/img/exp3_mnist_fashion.png | Bin 111594 -> 110311 bytes text/img/exp5.png | Bin 33597 -> 33987 bytes 4 files changed, 140 insertions(+), 97 deletions(-) diff --git a/text/hlavac-thesis.bib b/text/hlavac-thesis.bib index 08ffcd6..be84adf 100644 --- a/text/hlavac-thesis.bib +++ b/text/hlavac-thesis.bib @@ -134,4 +134,9 @@ @inproceedings{mckinney2010data year={2010}, organization={Austin, TX} } +@article{grill2016combining, + title={Combining network anomaly detectors}, + author={Grill, Martin}, + year={2016} +} diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 171c78b..8f4a1d5 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -27,6 +27,8 @@ \usepackage{todonotes} +\usepackage{tabularx} + \usepackage{amssymb} \usepackage{algorithm, algcompatible} %for for creating algorithm figures @@ -35,7 +37,7 @@ \algdef{SE}[DOWHILE]{DO}{DOWHILE}{\algorithmicdo}[1]{\algorithmicwhile\ #1}% \newcommand{\specialcell}[2][l]{% -\begin{tabular}[#1]{@{}l@{}}#2\end{tabular}} +\begin{tabularx}{0.2\linewidth}[#1]{@{}l@{}}#2\end{tabularx}} \usepackage{amsmath} @@ -46,9 +48,6 @@ % \iflanguage{czech}{\renewcommand*{\acronymname}{Seznam pou{\v z}it{\' y}ch zkratek}}{} % \makeglossaries -% ------------- Colors abbreviations ------------ -\def\cbl{\color{blue}} %VH's suggestions -\def\cre{\color{red}} %JH's discussion % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % EDIT THIS % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % @@ -60,12 +59,14 @@ \author{Jaroslav HlaváÄ} %author's name without academic degrees \authorWithDegrees{Jaroslav HlaváÄ} %author's name with academic degrees \supervisor{Ing. Martin Kopp} -\acknowledgements{THANKS (remove entirely in case you do not with to thank anyone)} %TODO -\abstractEN{Summarize the contents and contribution of your work in a few sentences in English language.} -\abstractCS{V n{\v e}kolika v{\v e}t{\' a}ch shr{\v n}te obsah a p{\v r}{\' i}nos t{\' e}to pr{\' a}ce v {\v c}esk{\' e}m jazyce.} %TODO + \acknowledgements{I would like to express my thanks to my supervisor Ing. Martin Kopp for the inspiration I always brought from our meetings, for the opportunity to work on a real problem and last but not least his patience.} +\abstractEN{Precise and fast network behavior anomaly detection highly depends on the models created for the network monitored. Whole network models tend to be full of hosts with very diverse behavior. Creating a model for each host is often not possible, because each host does not generate enough traffic. Network hosts can be separated into smaller groups by community-based clustering. On big networks of tens of thousands, these clusters can contain several thousands of hosts. + +This thesis explores the options of finding a representative selection as a cluster prototype for each cluster in Cognitive Targeted Anomaly Detection Framework. Because of the dynamic nature of networks, only a pair-wise similarity exists between each host pair. This is why representative selection methods for topological spaces have to be used. A modification to $\delta$-Medoids algorithm is proposed with the ability to select a low number of representatives while maintaining the best possible coverage of the cluster. This algorithm improves the coverage of clusters as compared to the currently used method, selecting the representatives randomly.} +\abstractCS{V n{\v e}kolika v{\v e}t{\' a}ch shr{\v n}te obsah a p{\v r}{\' i}nos t{\' e}to pr{\' a}ce v {\v c}esk{\' e}m jazyce.} \placeForDeclarationOfAuthenticity{Prague} -\keywordsCS{VýbÄ›r reprezentantů, Detekce anomálií, Komunitní clusterování, Systém detekce naruÅ¡ení, $\delta$-medoids, Topologický prostor, Párová podobnost} %TODO -\keywordsEN{Representative selection, Network anomaly detection, Community-based clustering, Intrusion detection system, $\delta$-medoids, Topological space, Pair-wise similarity} %TODO +\keywordsCS{VýbÄ›r reprezentantů, Detekce anomálií, Komunitní clusterování, Systém detekce naruÅ¡ení, algoritmus $\delta$-Medoids, Topologický prostor, Párová podobnost} %TODO +\keywordsEN{Representative selection, Network anomaly detection, Community-based clustering, Intrusion detection system, $\delta$-Medoids algorithm, Topological space, Pair-wise similarity} %TODO \declarationOfAuthenticityOption{1} %select as appropriate, according to the desired license (integer 1-6) % \website{http://site.example/thesis} %optional thesis URL @@ -77,6 +78,7 @@ \setsecnumdepth{part} \chapter{Introduction} +\todo{translate abstract to czech after checking it} The Internet became the dominant medium for communication several decades age, and there are still many issues concerning its security. Companies all around the world connect more and more of their infrastructure to the Internet. @@ -202,17 +204,19 @@ \section{Network Anomaly Detection}\label{sec:nbad} Network anomaly detection system creates a baseline model of the network behavior, which is then compared to the actual traffic. Any sudden change is considered to be an anomaly that is reported by the IDS. -\section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} \todo{prepsat prvni dva odstavce lepe} -Having access to all the traffic in a network does not necessarily mean that every anomaly that occurs can be detected. -Not every network host generates enough traffic to create a precise model of its behavior. -Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. -\todo{Explain better, what is the goal of anomaly detection, Grill Thesis - toto ma ted malou prioritu} +\section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} + +There are several challenges in anomaly-based network intrusion detection. +A huge volume of data needs to be processed very fast to keep the real-time reaction rate to anomalies. +Also, each normal behaviour that is identified as anomalous creates unwanted load for postprocessing of the occurence. +Having a big amount of these false positives slows the process down. +Finally there is a lack of labeled training data to create models of wanted network behavior ~\cite{grill2016combining}. -That is why models are calculated for whole networks where it is possible to create a model that reflects the behavior of the network. -This method is widely used, and very efficient on smaller (several devices) networks. -However, capturing flow data from a big network of thousands of devices can result in too much information. -One can easily miss one infected device generating different traffic. -The noise in the collected data is too high. +Therefore, even having access to all the traffic in a network does not necessarily mean that every anomaly that occurs can be detected. +Maintaining a model for each host is not an option as not every network host generates enough traffic to create a precise model of its behavior. +Creating a behavioral model for the whole network is not a very good option either. +The behavior of each host is different and combining them together leads to a very diverse model. +A small amount of infected devices or a single intrusion attempt can be easily missed in this model due to the volume of information given by the rest of the network. To give an example, imagine detection of a single infected device, such as a printer in a 10 story office building. This printer, being a part of a botnet, was ordered to generate traffic for DDoS (Distributed Denial of Service) attacks. @@ -272,6 +276,7 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: \begin{equation} +\label{eq:kopp} \textrm{sim}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:, \tag{2} \end{equation} where $F^a$, $F^b$ represent the frequency and $S$ represents the union of sets of servers visited by the network hosts $a$ and $b$. @@ -302,7 +307,8 @@ \chapter{Theoretical Overview and State of the Art}\label{ch:theory} \section{Introduction to Clustering}\label{sec:clust_intro} As defined in \cite{guttag2016introduction}, clustering is an unsupervised machine learning method that organizes objects into groups so that each group consists of members that are similar in some way. Without any prior knowledge of the data, this method looks for structures in feature vectors. -Variability can be calculated for each cluster \textit{c}, and it shows how much objects in given cluster differ. +For each cluster $c$, a variability can be calculated. +It shows how much objects in given cluster differ. Variability and dissimilarity are two properties defined for a better understanding of the data. Variability is defined as \begin{equation} @@ -344,11 +350,11 @@ \section{Introduction to Clustering}\label{sec:clust_intro} $K$-means randomly chooses $K$ centroids in the space of the dataset and then assigns each point to a centroid. After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. The algorithm stops when the centroids of clusters stop changing. -Algorithm~\ref{alg:kmeans} shows the pseudocode of $K$-means algorithm. +Algorithm~\ref{alg:k_mean_pseudocode} shows the pseudocode of $K$-means algorithm. -\begin{algorithm}[t]\label{alg:kmeans} +\begin{algorithm}[t] \caption{$K$-means} - \label{k_mean_pseudocode} + \label{alg:k_mean_pseudocode} \begin{algorithmic}[1] \INPUT data $X = x_0, x_1, ..., x_n$; number of clusters $k$ \OUTPUT $k$ clusters; $k$ centroids @@ -385,19 +391,23 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Next section delves deeper into what it means when a measure does not form a metric space. \section{Clustering in Topological Space}\label{sec:cluster_topo} -This section explains the difference between metric and topological spaces and clustering approaches for topological spaces. +This section explains how does clustering approaches differ in topological spaces. +A metric space is a topological space with special properties, that are given in its definition. +Therefore, each clustering algorithm that works on a topological space will work on a metric space also. +However, there are algorithms that give better results in metric spaces that will not work by definition on a topological space, i.e. $K$-Means algorithm. -A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping $X \times X \to \mathbb{R}$ which satisfies the following conditions: +A metric space (see e.g. \cite{choudhary1992elements}) is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping $X \times X \to \mathbb{R}$ which satisfies the following conditions: \begin{enumerate} \item [(i)] $d(x, y) \geq 0$; \item [(ii)] $d(x, y) = 0 \iff x = y$; \item [(iii)] $d(x, y) = d(y, x)$ - \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \mathrm{for} x, y, z \in X$. + \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \ \mathrm{for} \ x, y, z \in X$. \end{enumerate} Any function $d$ following these conditions is called distance. \todo{is this realy true?} -The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctadf} does not fulfill the last point of the definition above. Consequently, it is not a distance and does not form a metric space. Furthermore, it is a pairwise similarity that forms a subspace for comparing each pair of samples. -This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. +The similarity measure in Equation~\ref{eq:kopp} does not fulfill the last point of the definition above, as is explained in Section~\ref{sec:ctadf}. +Consequently, it is not a distance and does not form a metric space. Furthermore, it is a pairwise similarity that forms a subspace for comparing each pair of samples. +This is why that similarity measure forms a topological space, which is defined as follows (see e.g., \cite{stahl2014introduction}). Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$, and with the following properties: \begin{enumerate} @@ -408,17 +418,14 @@ \section{Clustering in Topological Space}\label{sec:cluster_topo} \end{enumerate} The ordered pair ($S, F$) is called a topological space. -\todo{explain about neighborhoods??} - -$K$-means clustering from the previous section cannot be used in topological space as the centroids do not exist. -In many cases, there is no prior knowledge of the desired number of clusters $K$, as is the case in this thesis. +$K$-means clustering from the previous section cannot be used, because centroids do not exist in topological spaces. A popular method that is used for clustering in non-metric spaces is called community-based clustering. Community-based clustering detects communities in the data \cite{kopp2018community}. A community is a subset of examples in the data, that is densely connected with each other. One of the ways how to detect communities in a graph is to create a full-adjacency matrix. This matrix contains all connections between all nodes in the given graph. -Analyzing the matrix then can tell us about the densities in different parts of graphs. +Analyzing the matrix can tell us about the densities in different parts of graphs. Louvain method is a similar approach to clustering when we do not have a simple graph but a set of samples and a pairwise similarity measure \cite{kopp2018community}. This method relies on creating a full similarity matrix for the whole dataset and then looking for communities in the data. @@ -445,9 +452,10 @@ \section{Representative Selection} \item Compression \item Efficient Finding Nearest Neighbors \end{itemize} -All of these apply to the NBAD aswell. +All of these apply to the NBAD. Therefore, summarizing the behavior of the whole cluster into a cluster prototype is desirable. -As is finding the smallest possible number of prototypes for each cluster for efficient finding nearest neighbors when adding new hosts to their corresponding clusters. +As is finding the smallest possible number of prototypes for each cluster for efficient finding nearest or most similar neighbors when adding new hosts to their corresponding clusters. + \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{sec:def_rep_selection} Representative selection aims to find a minimal subset of examples from a cluster, that carries sufficient information about the whole cluster. @@ -457,7 +465,7 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ The task is finding a representative subset $Z \subseteq S$ that best encapsulates the data. Two following requirements are imposed on an algorithm for finding a representative subset: \begin{itemize} - \item \textbf{Requirement 1}: The algorithm must return a subset $Z \subseteq S$ such that for any sample $x \in S$, there exists a sample $z \in C$ satisfying $d(x, z) \le \delta$. + \item \textbf{Requirement 1}: The algorithm must return a subset $Z \subseteq S$ such that for any sample $x \in S$, there exists a sample $z \in Z$ satisfying $d(x, z) \le \delta$. \item \textbf{Requirement 2} : The algorithm cannot rely on a metric representation of the samples in $S$. \end{itemize} To compare the quality of different subsets returned by different algorithms, two criteria are measured: @@ -469,7 +477,8 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ In addition, the following desiderata for a representative selection algorithm are expected. \begin{itemize} \item \textbf{Desideratum 1}: Stable representative selection algorithms are preferred. Let $Z_1$ and $Z_2$ be different representative subsets for dataset $S$ obtained by two different runs of the same algorithm. -Stability is defined as the overlap $\frac{|Z_1 \cap Z_2|}{|Z_1 \cup Z_2|}$ The higher the expected overlap is, the more stable the algorithm is. +Stability is defined as the overlap $\frac{|Z_1 \cap Z_2|}{|Z_1 \cup Z_2|}$. +The higher the expected overlap is, the more stable the algorithm is. This desideratum ensures the representative set is robust to randomization in data ordering or the choices made by the algorithm. \item \textbf{Desideratum 2}: The algorithm should be efficient and scale well for large datasets. @@ -478,11 +487,11 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ This definition of representative selection problem serves well for this paper. \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_metric} -A metric dataset has to have the same number of features for each sample and a metric that fulfills all the requisites in the definition of the metric space. +A dataset that hase the same number of features for each sample and there is a metric metric that fulfills all the requisites in the definition of the metric space is a metric dataset. Selecting a prototype from this dataset is often best achieved by calculating a centroid for a given cluster. A centroid can be calculated as the mean of the points in the cluster. An example of a centroid in a cluster can be found in Figure~\ref{img:centroids} a). -Other ways of calculating medoids can be used, e.g. weighted average of all points. +Other ways of calculating centroids can be used, e.g. weighted average of all points. \begin{figure} \includegraphics[width=\linewidth]{img/centroids.png} @@ -496,7 +505,7 @@ \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_met Formally, medoid is defined as: \begin{equation} x_{medoid} = \mathrm{argmin}_{y \in \{x_1, x_2, ..., x_n\}} \sum_{i=1}^{n}{d(y, x_i)}\:, \tag{5} -\end{equation} \todo{zavest matematicky symbol pokud chci pouzitlimits} +\end{equation} where $x_1, x_2, ..., x_n$ is a set of $n$ points in a space with a distance function $d$. In Figure~\ref{img:medoids}, medoids were chosen instead of centroids. \begin{figure} @@ -506,16 +515,16 @@ \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_met \end{figure} \subsection{Representative Selection in a Topological Space}\label{sec:rep_select_arbitrary} -Clearly, the concept of centroid explained in the previous section cannot be used to solve the problem of finding a representative in an arbitrary space (i.e., topological space). -Medoids, of course, can be selected for datasets in arbitrary spaces. +According to the previous section, the concept of centroid cannot be used to solve the problem of finding a representative in an arbitrary space (i.e., topological space). +Instead, medoids can be selected for datasets in arbitrary spaces. $K$-Medoids algorithm resembles $K$-means algorithm in breaking the dataset into $K$ groups. In these groups it finds a medoid that is minimizes the distance to each other point in the group. $K$-Medoids is most commonly used for representative selection in topological spaces. -There are not many more well-explored methods for solving this problem. +There are not many other well-explored methods for solving the problem of finding medoids. The one that is used in this thesis is explained in \cite{liebman2015representative}. -They took the main ideas of $K$-means clustering and transformed them for usage in a non-metric space with a pair-wise similarity measure. +The main ideas from $K$-means clustering were taken and transformed for usage in a non-metric space with a pair-wise similarity measure. Instead of stating the $K$ in advance they state a parameter $ 0 \leq \delta \leq 1$ that serves as a constraint. Then they separate the cluster into subclusters based on this parameter. Each of these subclusters is represented by a medoid. @@ -523,7 +532,7 @@ \subsection{Representative Selection in a Topological Space}\label{sec:rep_selec This method is explained in greater detail in the Section~\ref{subsec:delta_medoids}. \section{Algorithms Relevant for Topological Space}\label{sec:relevant_methods} -Based on the previously explained approaches for finding representatives of clusters in non-metric, spaces the following methods were chosen, tested and compared as a part of this thesis. +Based on the previously explained approaches for finding representatives of clusters in non-metric spaces, the following methods were chosen, tested and compared as a part of this thesis. \begin{itemize} \item Random selection \item Greedy Selection @@ -537,7 +546,7 @@ \subsection{Random Selection}\label{subsec:random_select} It selects a given number of representatives from the cluster randomly. In dense clusters, selecting random samples from each cluster leads to undesired noise. -However as experimental results presented in \cite{kopp2018community} in the domain of computer networks the density of clusters is not significant. +However as experimental results presented in \cite{kopp2018community} in the domain of computer networks, the density of clusters is not significant. It, of course, depends on each network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. \subsection{Greedy Selection}\label{subsec:greedy_select} @@ -550,7 +559,7 @@ \subsection{Greedy Selection}\label{subsec:greedy_select} In the worst case scenario, all points would be selected as representatives while always calculating the distance to each remaining sample in the set. Then it would reach the time complexity of $\mathcal{O}(n^2)$. \subsection{$\delta$-Medoids}\label{subsec:delta_medoids} -The two following algorithms were first eplained in \cite{liebman2015representative}. +The two following algorithms were first explained in \cite{liebman2015representative}. They were tested on two different problems that also use clustering in non-metric space - computing distance of two musical segments and comparing trajectories of objects. $\delta$-Medoids algorithm tries to find a minimal subset of points that are needed to cover the cluster with only one given constraint, that distance of the representatives would be at least $\delta$. @@ -562,7 +571,7 @@ \subsection{$\delta$-Medoids One-Shot} The idea behind this algorithm is to go through the dataset looking for representatives that differ at least by the given parameter $\delta$ from each previously selected representatives. By taking this approach, it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. -The algorithm is shown in Algorithm~\ref{alg:delta_medoids_one_shot}. +The pseudocode of this algorithm is shown in Algorithm~\ref{alg:delta_medoids_one_shot}. It have been optimized for better memory efficiency as opposed to the version in the cited paper. The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. @@ -658,7 +667,7 @@ \subsection{$\delta$-Medoids Modified} \caption{ReduceClusters} \label{alg:reduce} \begin{algorithmic}[1] - \INPUT $m$ subclusters $X$ = $x_i, ..., x_m$; their representatives $R = r_0, ..., r_m$; whole cluster size$s$ + \INPUT $m$ subclusters $X$ = $x_i, ..., x_m$; their representatives $R = r_0, ..., r_m$; whole cluster size$s$; constant $\kappa$ \STATE $threshold = 0.01s$ \STATE Initiate $i = 0$ \IF{$|X| > \kappa$} @@ -716,7 +725,7 @@ \section{Datasets Created for this Work} \label{img:datasets} \end{figure} -The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter~\ref{ch:theory}. +The Blobs datasets are very easy to represent with a centroid and were tested whether and how many points is needed to represent them using methods explained in Chapter~\ref{ch:theory}. The Overlap dataset consists of 3 clusters in shapes of blocks. These blocks overlap partly to test how selected algorithms cope with overlapping clusters. Circles 3D and Moons are one of the hardest datasets to represent even by methods that are designed specifically for the metric space. @@ -742,7 +751,7 @@ \section{Image Recognition Datasets} \section{Network Security Dataset}\label{sec:real-data} Real network traffic captured from a company with approximately 20 thousand employees. -The capture is from 24 hours of a working day and consists of \todo{count flows} flows. +The capture is from 24 hours of a working day and consists of 33.5 milion flows. Proxy logs were used to capture the connections. Hosts that appeared in that capture were clustered using the method explained in Section~\ref{sec:cluster_topo}. These clusters with all hosts labeled were used as real network dataset in this thesis. @@ -752,19 +761,22 @@ \section{Network Security Dataset}\label{sec:real-data} %========================================CHAPTER================================== \chapter{Experiments}\label{ch:experiments} -In this chapter each experiment that was run. +In this chapter, all the experiments that were run are described. Each section is dedicated to one experiment. For each experiment, the motivation, experimental setup, results and conclusion are listed. All experiments were made in Jupyter Notebook technology using Python 3.7 kernel. For loading data from files and storing them in memory the Pandas library version 0.23.4 was used \cite{mckinney2010data}. +The algorithms $K$-Nearest Neighbors with $K=1$ was used for classification of the test data based on the cluster prototypes created by selected algorithms. +For each experiment a confusion matrix was created from this test classification. \section{Visualizable datasets}\label{sec:exp1} -The first experiment was run on the data specifically created for this thesis. +The first experiment was run on the visualizable datasets specifically created for this thesis. +Seeing clustering results on visualizable datasets provides humans the insight into algorithms performance. The methods that were explained in Section~\ref{sec:relevant_methods} are designed to work in non-metric space but they still work in a metric space. -Using them on visualisable benchmark datasets with known problems helped to determine their properties. +Using them on visualisable benchmark datasets with known problems helped to determine their properties such as how many representatives each algorithm selects and how well does the representatives work in classification in newly added samples. \subsection{Experimental Setup} Datasets used for this experiment were: @@ -772,30 +784,36 @@ \subsection{Experimental Setup} \item Blobs 3D \item Overlap \item Moons - \item Noisy Circles 3D + \item Circles 3D \end{itemize} -All 5 algorithms were run. +All five algorithms were run. The $\delta$ was estimated as 5\% quantile of distances from one random point in the data to all of the others. The similarity measure used in these datasets was Euclidean distance. +This experiment was performed in five runs using cross-validation. +Each dataset was split to five 20\% parts. +In each run four parts were combined to form the training data and the remaining part was used to simulate newly added samples to be classified to their corresponding cluster. + \subsection{Results} The results for each dataset are listed below. -\todo{make references to each table and picture or put hard placement for them?} +A table with the number of representatives selected from each cluster is presented as well as confusion matrix with correct and incorrect classifiactions of each test sample. +\medskip \textbf{Blobs 3D} - -\begin{table}[t] +\begin{table}[H] +\resizebox{\textwidth}{!}{ \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline A & 400 & 21 & 23 & 25 & 16 & 16 \\ B & 400 & 17 & 20 & 23 & 20 & 20 \\ C & 400 & 19 & 20 & 22 & 20 & 20 \\ \hline -\end{tabular} +\end{tabular}} \caption{Number of selected representatives for Blobs 3D dataset} \end{table} +\noindent The confusion matrix can be seen in Figure~\ref{img:exp1_blobs} \begin{figure}[t] \includegraphics[width=\linewidth]{img/exp1_blobs.png} @@ -805,16 +823,18 @@ \subsection{Results} \textbf{Overlap} -\begin{table}[t] +\begin{table}[H] +\resizebox{\textwidth}{!}{ \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline A & 800 & 25 & 23 & 23 & 23 & 23 \\ B & 960 & 26 & 21 & 23 & 21 & 21 \\ C & 640 & 22 & 24 & 24 & 24 & 24 \\ \hline -\end{tabular} +\end{tabular}} \caption{Number of selected representatives for Overlap dataset} \end{table} +\noindent The confusion matrix can be seen in Figure~\ref{img:exp1_overlap} \begin{figure}[t] \includegraphics[width=\linewidth]{img/exp1_overlap.png} @@ -824,13 +844,14 @@ \subsection{Results} \textbf{Moons} -\begin{table}[t] +\begin{table}[H] +\resizebox{\textwidth}{!}{ \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline A & 600 & 22 & 21 & 22 & 21 & 21 \\ B & 600 & 21 & 24 & 25 & 24 & 24 \\ \hline -\end{tabular} +\end{tabular}} \caption{Number of selected representatives for Moons dataset} \end{table} @@ -839,18 +860,21 @@ \subsection{Results} \caption{Confusion matrices for Moons dataset} \label{img:exp1_moons} \end{figure} +\noindent The confusion matrix can be seen in Figure~\ref{img:exp1_moons} \textbf{Circles} -\begin{table}[t] +\begin{table}[H] +\resizebox{\textwidth}{!}{ \begin{tabular}{l|llllll} \hline \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline A & 600 & 15 & 13 & 13 & 13 & 13 \\ B & 600 & 6 & 8 & 8 & 8 & 8 \\ \hline -\end{tabular} +\end{tabular}} \caption{Number of selected representatives for Circles dataset} \end{table} +\noindent The confusion matrix can be seen in Figure~\ref{img:exp1_circles} \begin{figure}[t] \includegraphics[width=\linewidth]{img/exp1_circles.png} @@ -872,7 +896,7 @@ \subsection{Conclusion for Visualizable Datasets} On these two datasets the Random Selection algorithm clearly peforming worse. \todo{clearly from confusion matrices, how to write this} From the results listed the difference between $\delta$-Medoids Full and Modified is not easily distinguishable. -The difference between these two algorithms can be seen on Figure~\ref{img:difference_modified}. +The difference between these two algorithms can be seen in Figure~\ref{img:difference_modified}. The full version of the algorithm tends to select the border samples of dataset as representatives. In these datasets the results do not show this because they are relatively dense and have normal distribution. On datasets that are more sparse this leads to a high number of representatives selected. @@ -887,7 +911,7 @@ \section{Image Recognition Datasets}\label{sec:exp3} After the algorithms were tested on visualizable data, the experiments with well known labeled datasets could start. The motivation for this experiment was to test the algorithm results on well explored and well-annotated datasets commonly used as a benchmark for clustering. -In this and the following experiments only the $\delta$-Medoids Modified was tested. +In this and the following experiments, the $\delta$-Medoids Full algorithm was not used and only the Modified algorithm was tested. \subsection{Experimental Setup} Datasets used for this experiment were: @@ -904,8 +928,8 @@ \subsection{Results} The results for different methods are listed below. \textbf{Pendigits} -\todo{make references to each table and picture or put hard placement for them?} -\begin{table}[t] +\begin{table}[H] +\resizebox{\textwidth}{!}{ \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -919,9 +943,10 @@ \subsection{Results} 7 & 778 & 22 & 20 & 20 & 20 \\ 8 & 719 & 75 & 77 & 15 & 15 \\ 9 & 719 & 50 & 52 & 13 & 13 \\ \hline -\end{tabular} +\end{tabular}} \caption{Number of selected representatives for Pendigit dataset} \end{table} +\noindent The confusion matrix can be seen in Figure~\ref{img:exp3_pendigits} \begin{figure}[t] \includegraphics[width=\linewidth]{img/exp3_pendigits.png} @@ -934,7 +959,8 @@ \subsection{Results} In the experiment for this dataset the Greedy Selection algorithm was omitted as the experiments would take too long on clusters that contain 6000 samples. Previous experiments show that the $\delta$-Medoids agorithm chooses similar amount of representatives while getting better coverage. -\begin{table}[t] +\begin{table}[H] +\resizebox{\textwidth}{!}{ \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -948,10 +974,10 @@ \subsection{Results} 7 & 6000 & 266 & 19 & 19 \\ 8 & 6000 & 1971 & 16 & 16 \\ 9 & 6000 & 686 & 28 & 28 \\ \hline - -\end{tabular} +\end{tabular}} \caption{Number of selected representatives for MNIST Fashion dataset} \end{table} +\noindent The confusion matrix can be seen in Figure~\ref{img:exp4} \begin{figure}[t] \includegraphics[width=\linewidth]{img/exp3_mnist_fashion.png} @@ -961,27 +987,31 @@ \subsection{Results} \subsection{Conclusion for Image Recognition Datasets} Using algorithms on real datasets shows more significant differences between results in algorithms. -Especially the difference in numbers of representatives chosen for different methods. +Especially the difference in numbers of representatives chosen with $\delta$-Medoids Modified while still perforimning the best in classification test. The $\delta$-Medoids Modified choses the lowest number of representatives while getting the best results. Greedy Selection and $\delta$-Medoids One-Shot select similar number of representatives and their results are also very similar. -This is interesting as the complexity of these algorithms differs a lot, Greedy Selection being much slower with $\mathcal(n^2)$. +This is interesting as the complexity of these algorithms differs a lot, Greedy Selection being much slower with $\mathcal{O}(n^2)$. \section{Network Security Data}\label{sec:exp4} +This experiment tests the functionality of selected methods as if they were plugged into the toolchain of Cognitive Targeted Anomaly Detection described in Section~\ref{sec:ctadf}. +The data for it were collected from the framework and the experiment was made separately on a different machine. + \subsection{Experimental Setup} -For this experiment clustered hosts from a real network were used. +Clustered hosts from a real network were used for this experiment. This dataset is explained in greater detail in Section~\ref{sec:real-data}. From this dataset traffic for first 4 hours was used for training the model and the next 4 hours were used for testing the representatives. The similarity measure from Section~\ref{sec:ctadf} was used. -All 4 algorithms were run. +Four algorithms were run, $\delta$-Medoids Full was omitted for reasons listed before. The $\delta$ was set to 0.8 as was the threshold used in community-based clustering for their creation. \subsection{Results} The results for different methods are listed below. -\begin{table}[t] +\begin{table}[H] +\resizebox{\textwidth}{!}{ \begin{tabular}{l|lllll} \hline \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline @@ -1001,9 +1031,11 @@ \subsection{Results} N & 127 & 91 & 91 & 34 & 34 \\ O & 67 & 48 & 48 & 48 & 48 \\ P & 50 & 50 & 50 & 50 & 50 \\ \hline -\end{tabular} +\end{tabular}} \caption{Number of selected representatives for real network data} \end{table} +\noindent The confusion matrix can be seen in Figure~\ref{img:exp4} +$\delta$-Medoids One Shot algorithm shows better results in classification as the number of representatives selected by this algorithm is significantly higher containing on average 30\% of each cluster. \begin{figure}[t] \includegraphics[width=\linewidth]{img/exp4.png} @@ -1011,27 +1043,34 @@ \subsection{Results} \label{img:exp4} \end{figure} +\todo{add average precision and recall values to results} + \subsection{Conclusion for Network Security Data} The results show that the $\delta$-Medoids algorithm is significantly better than selecting random representatives from the cluster. -Even for clusters with several thousand of samples few samples were selected a while retaining good properties for assigning points to clusters. -The downside is the speed of the algorithm but this was not a part of this experiment. -\todo{expand the conclusion} +The classification routine was able to classify most of the test samples correctly for it. +In most of the clusters the methods perform in a very similar way. +The difference can clearly be seen in cluster H, that is complex and the $\delta$-Medoids algorithm was able to represent it the best. +Even for clusters with several thousands of samples few samples were selected a while retaining good properties for assigning points to clusters. +The downside is the speed of the algorithm but this was not a part of this experiment and is already explained in Section~\ref{sec:relevant_methods}. -\section{Average Fit of Data}\label{sec:exp5} +\todo{expand the conclusion} + +\section{Fit of Data}\label{sec:exp5} -In this experiment the average data fit as defined in Section~\ref{sec:representative_selection} was tested. +In this experiment the data fit as defined in Section~\ref{sec:representative_selection} was tested. +The motivation was to test how many samples are covered by each added sample and whether on average the $\delta$-Medoids algorithm performs better than Random Selection algorithm. \subsection{Experimental Setup} -In this experiment one complex cluster (cluster 'C') from Network Security Dataset was represented by $\delta$-Medoids and Random Selection algorithms. +In this experiment one, complex cluster (cluster 'C') from Network Security Dataset was represented by $\delta$-Medoids and Random Selection algorithms. After the representatives were selected, the percentage of coverage by each representative was calculated and plotted into graph. - +\todo{Make average fit or at least give more examples} \subsection{Results} -\begin{figure}[t] +\begin{figure}[H] \includegraphics[width=\linewidth]{img/exp5.png} - \caption{\cbl Average fit of representatives in one cluster} + \caption{Average fit of representatives in one cluster} \label{img:exp5} \end{figure} @@ -1047,7 +1086,7 @@ \subsection{Conclusion for Fitting the Data} \chapter{Conclusion} -A method to represent clusters in non-metric space by a small subset of the whole cluster was implemented and tested in this thesis. +The method to represent clusters in non-metric space by a small subset of the whole cluster was implemented and tested in this thesis. This method was adjusted so that it can be incorporated into Cognitive Targeted Anomaly Detection Framework in the future. Methods that are relevant for creating cluster prototypes in a non-metric space were studied. @@ -1057,16 +1096,14 @@ \chapter{Conclusion} All algorithms were then further tested on image recognition datasets Pendigits and MNIST Fashion. Finally, the methods were fine-tuned and tested on a real Network Security Dataset captured in a medium-sized company. -The experiments that were performed have shown that the $\delta$-Medoids Modified fulfills the requirements the best. -It selects a small number of representatives from each cluster and is more precise than currently used Random Selection algorithm. -The average fit by representatives of the data was also tested. -A cluster prototype created by $\delta$-Medoids algorithm fits clusters significantly better than randomly selected representatives. +The results presented in this thesis show that the modification done to $\delta$-Medoids improve its performance on network security datasets. +The $\delta$-Medoids Modified algorithm is able to find a small number of representative with a decent coverage of the whole dataset. +Representatives selected this way do fit the dataset on average better than selecting them randomly, which is the method currently used. -This thesis explored the nature of the clusters collected on the Internet. \todo{jak zminit possible future work s kNN graphs atd.} \bibliographystyle{iso690} -\bibliography{hlavac-thesis.bib} +\bibliography{hlavac-thesis.bib} \todo{check bibliography correctness} \setsecnumdepth{all} \appendix @@ -1080,7 +1117,8 @@ \chapter{Abreviations} \item[DPI] Deep Packet Inspection \item[IDS] Intrusion Detection System \item[NBAD] Network-Based Anomaly Detection - \item[NIDS] Intrusion Detection System + \item[NIDS] Network Intrusion Detection System + \item[RFC] Request for Comment \end{description} diff --git a/text/img/exp3_mnist_fashion.png b/text/img/exp3_mnist_fashion.png index bc9b1539ca7aa379d0897fc4eaa591676d6feec0..30d746a8e3d16400841c65470ae6f14835c6967a 100644 GIT binary patch literal 110311 zcmcG#1z23o(k8VC>^f;$Nz1b24^m%-g31cwmZLvR@+FhH=uNq`V^kiiM= zI_R*I_kI8W{q{cJ-raBSUY?nUo<4QDyQ-_}RQ;+>jJm2kKJHUo6ciMEg?Dc>QBcqU z$glY$EM!k%NnIK8ACBug0}m7wJd!_ORFv#oN)!}KVtZ|UPkj|-F@TFRw}q99r8T#& 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z;XYrD>-^3*jy9og%as@KV1V*YFZ!uNEdKkg00PH`25qGy01m_pHq-eguG|ykgABm_ zAZiK-1avBQTc`=I*PlX+o#N&#)boGARuE6BHr*!vl51dH_PuF85g;}HoW{9-k#<|6 z1Tmdp%R)8&UtP~XqlP+SKi@lNj$Mfb<-%P5nS|KjQ#GP^xNhT}6XhG&@kh_a9N8dBg=5_TcubYIpo2>b4dC jn^^z<5(*-KfA6|Rj?}vse&rOK4uH)R1V_bZoFo4S&VNeC literal 0 HcmV?d00001 From ae1f7b3b2b04c05bd0e59c95f76c979d79d6c84b Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 12 May 2019 08:22:18 +0200 Subject: [PATCH 53/65] added missing image --- text/hlavac-thesis.tex | 3 +-- text/img/complexity.png | Bin 0 -> 8778 bytes 2 files changed, 1 insertion(+), 2 deletions(-) create mode 100644 text/img/complexity.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 7b31aa7..6bbefe5 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -1140,8 +1140,7 @@ \subsection{Experimental Setup} \subsection{Results} \begin{figure}[H] -%VH \includegraphics[width=\linewidth]{img/exp5.png} - \includegraphics[width=\linewidth]{img/DummyFigure.pdf} + 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z`6nYU(@*1+TkcaDGpJdm@uVP`KucjHG9s=10k|<~cGbQ{lDeLucqk6}Qw~*DsXN|^ zMxSsU$lVMxUEB|LNuNJbC;T>EEbl!pA!4!Zg%zE&1sJDrJ2?U$ig$xVvb8Ih&+*_R zAY|Hch;+=A#!>7H=q*J$nr!U!2eJ-=q6|A>Y+841xQ1#OD4rWn`xQb}3_-Et-SDC< zFJrkCxY~sY&=5bo4!^oW(=kU=kPx`exU3`Pul)9(ip}HLfV_ffk(Wl Date: Sun, 12 May 2019 10:40:11 +0200 Subject: [PATCH 54/65] Introduction and Goals updated --- text/hlavac-thesis.tex | 80 +++++++++++++++--------------------------- 1 file changed, 29 insertions(+), 51 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 6bbefe5..eeb3880 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -76,8 +76,8 @@ \setsecnumdepth{part} \chapter{Introduction} -There are still many security issues on the Internet even several decades after it became the dominant medium for communication. -Companies all around the world connect more of their infrastructure to the Internet. +The Internet became the dominant medium for communication several decades age, and there are still many issues concerning its security. +Companies all around the world connect more and more of their infrastructure to the Internet. Each connected device is an opportunity for attackers. Security engineers are always inventing new methods to stop the attacks and keep their networks safe and secure. It is a never-ending race between attackers, trying to overcome new security methods, and defenders, the security personnel, trying to be always one step ahead of the attackers. @@ -86,30 +86,27 @@ \chapter{Introduction} Multi-layer defense systems are protecting contemporary networks. As attackers overcome one layer, there is yet another layer waiting for them. However, if the attackers succeed to bypass all of them unnoticed, a huge problem can occur. - Attackers can collect user credentials, exfiltrate data or gain control of devices without anybody's knowledge. It can take a long time before somebody notices the breach. A similar thing happened in Marriot International child company Starwood in 2018 \cite{hron2018breaches}. Names, emails, addresses and credit card numbers of 500 million customers were stolen in an attack that was discovered in 2018 but possibly could have begun as early as in 2014. -One of several methods that are used to detect such intrusions is detecting anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). +One of several methods that are used to detect such intrusions is looking for anomalies in the behavior of network hosts - Network Behavioral Anomaly Detection (NBAD). In NBAD, devices inside of the network are used as probes to gather information about the behavior of the network or individual hosts. -The method then compares the current behavior of the network to a model created from the historical data. -If a significant deviation in behavior occurs, the security team is notified and can act accordingly. +The current behavior of the network is then compared to a model created from the historical data. +If a significant deviation occurs, the security team is notified and can act accordingly. It is not uncommon for a company to have tens of thousands of devices and even more. -Straight forward usage of NBAD in these networks is not sufficient to detect breaches successfully. -For example, infection of a single device can be missed in the traffic of a medium sized company. +Using a single model for the whole network may be insufficient to detect breaches successfully. +For example, an infection of a single device can be missed in the traffic of a medium sized company. In order to detect such anomalies, it is needed to focus on smaller parts of the network. -Behavioral clustering of network hosts can be used to separate it into smaller pieces. +Behavioral clustering of network hosts can be used to divide it into smaller pieces. -In Cognitive targeted anomaly detection framework from Cisco, behavioral clustering is used to find groups of similar hosts in a network. -Each cluster created consists of hosts that have similar behavior. -The main focus of this work is to find a representation of these clusters that are used to calculate the model behavior. -The representation used in this thesis is based on cluster prototypes, which will be described in Chapter~\ref{ch:theory} +In Cognitive Targeted Anomaly Detection Framework from Cisco, behavioral clustering is used to find groups of similar hosts in a network. +The main focus of this thesis is to find a memory efficient representation of these clusters. +Terms representation of a cluster and cluster prototype are synonymous and are used interchangeably in this thesis. \todo{MK: is this better about cluster prototypes?} These cluster prototypes are then updated dynamically as the behavior of the network changes. - -Four algorithms previously used to represent clusters in computer vision or other fields were studied and modified for usage in Cognitive anomaly detection framework. +Four algorithms previously used to represent clusters were studied and modified for usage in Cognitive anomaly detection framework. They were tested and compared on both real and test datasets. The thesis is organized as follows. @@ -118,47 +115,27 @@ \chapter{Introduction} Chapter~\ref{ch:datasets} presents datasets used for testing. Chapter~\ref{ch:experiments} covers all the experiments with their results and is followed by the conclusion of this thesis. -%The main focus of this work is in representing these clusters found by clustering algorithms. -%Finding representation of clusters in Cognitive targeted anomaly detection framework is made more difficult by the fact, that similarity measure used for clustering does not form a metric space. -%Therefore the focus on methods that can be used for selecting representatives in non-metric spaces. -% -%Each cluster can have up to several thousands of hosts. -%Algorithms considered in this thesis were selected based on the following expectations. -%As the hosts are clustered based on their behavior, it is safe to expect that selecting a subset of the cluster can represent its behavior without losing significant information. -%Furthermore having fewer hosts as representatives of each cluster significantly reduce memory usage. - -%Why use clustering methods in network anomaly detection? -%Why do we need representative selection? -%What are the next sections about? -% -%Automatically grouping similar objects from a given set into clusters is a -%widely used machine learning technique in many fields ranging from -%recommendation engines and social network analysis to image segmentation and -%medical imaging. New applications of this powerfull method are found every year. -%Clustering is also used for Anomaly Detection in computer systems. \par -%In this work I collected 5TODO known methods used in different fields for -%cluster representation. What these methods have in common is possibility to use -%them in spaces where only generic similarity measure exists (these spaces do -%not necessarily need to be metric). +%========================================CHAPTER================================== \chapter{Goals} -This work aims to study methods used for the representation of network host behavioral clusters that can be applied in Cognitive targeted anomaly detection framework. +This work aims to study methods used for the representation of network host behavioral clusters that can be applied in Cognitive Targeted Anomaly Detection Framework. Behavioral similarity used for clustering in this framework does not form a metric space, which creates a requirement for algorithms used for representative selection. -This thesis should present a method that can be used for finding the cluster prototypes of clusters in non-metric space. -These cluster prototypes represent the whole cluster in a way that is convenient for further computation. +This thesis should present a method that can be used for finding the cluster prototypes of clusters in a non-metric space. +Cluster prototypes represent the whole cluster in a way that is convenient for further computation. It can be, for example, assigning newly added samples to their corresponding cluster. -Networks monitored by Cognitive targeted anomaly detection framework can have up to several hundreds of thousands of hosts which leads to massive clusters. +Networks monitored by Cognitive Targeted Anomaly Detection Framework can have up to several hundreds of thousands of hosts which leads to massive clusters. If the classification of newly added hosts were done by comparing the new host to all others, the memory and time needed for classification would be unmanageable. -That is why the number of representatives selected from each cluster should be kept as small as possible. +In the framework, each cluster is represented by a subset of its samples. +The number of representatives selected from each cluster should be kept as small as possible to maintain computational and memory efficiency. -General methods used for general cluster representation in non-metric spaces should be studied. -From these previously studied methods, the best ones will be selected and implemented. -As part of this thesis, a benchmark dataset for testing the chosen method should also be created. +Methods used for general cluster representation in non-metric spaces should be studied. +From these previously studied methods, the best-suited ones will be selected and implemented. +As part of this thesis, a benchmark dataset for testing the chosen methods should also be created. A further goal is to test the chosen methods on clustering datasets used by others and on a newly created benchmark dataset. -Based on this testing, the method should be found that suits best for use in this field and incorporates it into the Cognitive targeted anomaly detection framework. +Based on this testing, a method should be found that suits best the security field and can be incorporated into the Cognitive Targeted Anomaly Detection Framework. The selected method should be finetuned to get the best possible results in anomaly-detection. \setsecnumdepth{all} @@ -170,8 +147,8 @@ \chapter{Intrusion Detection System}\label{ch:ids} This chapter focuses on presenting ideas from network security needed to understand the main objective of this thesis. Different approaches to network intrusion detection systems (IDS) are explained in Section~\ref{sec:ids}. In Section~\ref{sec:nbad}, the scope is narrowed to network anomaly detection IDS and Section~\ref{sec:challenges}delves deeper into the challenges of it. -Section~\ref{sec:ctadf} serves as an introduction to Cognitive targeted anomaly detection framework used by Cisco. -Methods researched in this thesis were tested on real data collected from Cognitive targeted anomaly detection framework. +Section~\ref{sec:ctadf} serves as an introduction to Cognitive Targeted Anomaly Detection Framework used by Cisco. +Methods researched in this thesis were tested on real data collected from Cognitive Targeted Anomaly Detection Framework. \section{Types of Intrusion Detection Systems}\label{sec:ids} @@ -200,7 +177,7 @@ \section{Types of Intrusion Detection Systems}\label{sec:ids} \item \textbf{Anomaly-based} (behavior-based): A static or dynamic model of a given network is created over a period of time, and current network behavior is compared to expected (model) behavior for anomalies. \item \textbf{Stateful protocol analysis} (specification-based): The system keeps track of known protocols (e. g., pairing requests with replies) and finding unexpected behavior in these protocols. \end{itemize} -Cognitive targeted anomaly detection framework combines all of these approaches. +Cognitive Targeted Anomaly Detection Framework combines all of these approaches. This thesis focuses on the behavior-based part of the framework. \section{Network Anomaly Detection}\label{sec:nbad} @@ -316,7 +293,7 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} %========================================CHAPTER================================== -\chapter{Theoretical Overview}\label{ch:theory} +\chapter{Theoretical Overview and State of the Art}\label{ch:theory} This chapter introduces a theoretical background to explain clustering methods, the topic of non-metric spaces and the problem of representative selection. @@ -553,7 +530,7 @@ \section{Algorithms Relevant for Topological Space}\label{sec:relevant_methods} \subsection{Random Selection}\label{subsec:random_select} -Random selection algorithm serves as a baseline to measure the improvement as it is the method currently used in Cognitive targeted anomaly detection framework. +Random selection algorithm serves as a baseline to measure the improvement as it is the method currently used in Cognitive Targeted Anomaly Detection Framework. It selects a given number of representatives from the cluster randomly. In clusters dense clusters, selecting random samples from each cluster leads to undesired noise. @@ -1179,6 +1156,7 @@ \chapter{Abreviations} % \printglossaries \begin{description} %VH I changed the order of NBAD and NIDS to keep alphabetical order + \item[IDS] Intrusion Detection System \item[NBAD] Network-Based Anomaly Detection \item[NIDS] Intrusion Detection System \end{description} From d1eea01608bfe4eb18e3379a2e704ac10c14e990 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 12 May 2019 14:32:59 +0200 Subject: [PATCH 55/65] after MK comments --- text/hlavac-thesis.tex | 128 +++++++++++++++++++++-------------------- 1 file changed, 66 insertions(+), 62 deletions(-) diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index eeb3880..67be0f9 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -37,6 +37,8 @@ \newcommand{\specialcell}[2][l]{% \begin{tabular}[#1]{@{}l@{}}#2\end{tabular}} +\usepackage{amsmath} + %\usepackage[main=english,czech]{babel} % % list of acronyms @@ -144,7 +146,7 @@ \chapter{Goals} \chapter{Intrusion Detection System}\label{ch:ids} -This chapter focuses on presenting ideas from network security needed to understand the main objective of this thesis. +This chapter presents the ideas from network security needed to understand the main objective of this thesis. Different approaches to network intrusion detection systems (IDS) are explained in Section~\ref{sec:ids}. In Section~\ref{sec:nbad}, the scope is narrowed to network anomaly detection IDS and Section~\ref{sec:challenges}delves deeper into the challenges of it. Section~\ref{sec:ctadf} serves as an introduction to Cognitive Targeted Anomaly Detection Framework used by Cisco. @@ -200,8 +202,8 @@ \section{Network Anomaly Detection}\label{sec:nbad} Network anomaly detection system creates a baseline model of the network behavior, which is then compared to the actual traffic. Any sudden change is considered to be an anomaly that is reported by the IDS. -\section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} -Even if it was possible to capture all the traffic in the world and use it for anomaly detection, it would still be impossible to achieve 100\% precision. +\section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} \todo{prepsat prvni dva odstavce lepe} +Having access to all the traffic in a network does not necessarily mean that every anomaly that occurs can be detected. Not every network host generates enough traffic to create a precise model of its behavior. Thankfully absolute precision is not the goal of anomaly detection, as it serves as one of many layers of security in computer systems. \todo{Explain better, what is the goal of anomaly detection, Grill Thesis - toto ma ted malou prioritu} @@ -231,7 +233,7 @@ \section{Challenges of Real-Time Anomaly Detection}\label{sec:challenges} However, a network anomaly detection system should avoid a big overhead in false positives as it could overwhelm the system. In conclusion, a good anomaly detection system should detect all anomalies that are somehow connected to malicious behavior while trying to keep the number of false positives alerts as small as possible. -The small number of false positives that are reported is then double-checked in the following layers in the IDS. +The small number of false positives that are reported is then analyzed further in the following layers in the IDS. \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} @@ -256,7 +258,7 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} \end{itemize} The frequency is defined as: \begin{equation} -F_s^h = \frac{i}{n}\sum\limits_{1}{n}I(t_i, s, h)\:, +F_s^h = \frac{i}{n}\sum\limits_{1}{n}I(t_i, s, h)\:, \tag{1} \end{equation} where $n$ is the number of time windows, $I$ is the indicator function, which is 1 if the network host $h$ visited the server $s$ in the timewindow $t_i$ and 0 otherwise. This ratio-based frequency ensures that frequently visited servers (e.g., Google, Facebook) do not overshadow the less frequently visited servers. @@ -270,12 +272,12 @@ \section{Cognitive Targeted Anomaly Detection Framework}\label{sec:ctadf} The density of samples in sets of data is determined by cosine similarity of frequency vectors of two hosts. Similarity measure between hosts \textit{a} and \textit{b} is defined as: \begin{equation} -\textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:, +\textrm{cos}(a, b) = \frac{\sum\limits_{s \in S} F_s^a F_s^b} {\sqrt{\sum\limits_{s \in S} (F_s^a)^2} \sqrt{\sum\limits_{s \in S} (F_s^b)^2}}\:, \tag{2} \end{equation} -where $F_a$, $F_b$ represent the frequency and $S$ represents the union of sets of servers visited by the network hosts $a$ and $b$. +where $F^a$, $F^b$ represent the frequency and $S$ represents the union of sets of servers visited by the network hosts $a$ and $b$. After the clustering is done, only clusters that are bigger than 10 hosts are selected for representative selection. -Smaller clusters are analyzed using a fallback method - host-centered anomaly detection. +Smaller clusters are analyzed using a fallback method - host-centric anomaly detection. For the purpose of this work, we do not consider clusters smaller than 10 hosts. When clusters are determined, $\kappa$ random representatives are selected from each cluster. @@ -304,25 +306,24 @@ \section{Introduction to Clustering}\label{sec:clust_intro} Variability and dissimilarity are two properties defined for a better understanding of the data. Variability is defined as \begin{equation} -\mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:, +\mathrm{variability}(c) = \sum_{e \in c} \mathrm{distance}(\mathrm{mean}(c), e)^2\:, \tag{3} \end{equation} -\todo{are these equation numbers-corresponding to sections correct?} where \textit{e} is an object from given cluster. +where \textit{e} is an object from given cluster. The distance is a measure that quantifies the proximity of two objects with the same number of features. Many different distance measures are used in clustering. -Several of commonly used ones are: +Examples of commonly used ones are: \begin{itemize} \item Euclidean distance - \item Minkowski distance \item Manhattan distance + \item Mahanalobis distance \item Cosine similarity \end{itemize} -Cosine similarity measures the angle between two feature vectors. Dissimilarity is defined as \begin{equation} -\mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:, +\mathrm{dissimilarity}(C) = \sum_{c \in C} \mathrm{variability}(c)\:, \tag{4} \end{equation} -where $C$ stands for all clusters the data was clustered into. +where $C$ stands for a set of all clusters. For clustering, the aim is to keep the dissimilarity of all clusters from the dataset as low as possible. Given this definition, the best way to cluster every dataset would be to put each object to its cluster. That would not lead to any reasonable result. @@ -343,8 +344,9 @@ \section{Introduction to Clustering}\label{sec:clust_intro} $K$-means randomly chooses $K$ centroids in the space of the dataset and then assigns each point to a centroid. After creating these clusters, it calculates a new centroid for each cluster and then assigns the points in datasets to the new centroids. The algorithm stops when the centroids of clusters stop changing. -Figure~\ref{alg:kmeans} shows the pseudocode of $K$-means. -\begin{algorithm}[H]\label{alg:kmeans} +Algorithm~\ref{alg:kmeans} shows the pseudocode of $K$-means. + +\begin{algorithm}[t]\label{alg:kmeans} \caption{K-Means} \label{k_mean_pseudocode} \begin{algorithmic}[1] @@ -372,21 +374,20 @@ \section{Introduction to Clustering}\label{sec:clust_intro} \STATE \textbf{return} $clusters$, $centroids_t$ \end{algorithmic} -\end{algorithm} \todo{is this pseudocode looking better} +\end{algorithm} -This algorithm is fast, it has time complexity $\mathcal{O}(k \cdot n)$, where $n$ is the number of objects in a dataset and $d$ is the computation time of a distance between two points. +This algorithm is fast, it has time complexity $\mathcal{O}(k \cdot n)$, where $k$ is the number of clusters and $n$ is the number of objects in a dataset. It is the most common clustering algorithm as it typically converges in a few iterations. For more details about basic clustering algorithms see \cite{guttag2016introduction}. -%summary -The clustering as explained in this section has some restrictions that make it inapplicable for anomaly detection in this work. +The clustering as explained in this section has restrictions that make it make it inaplicable in Cognitive Targeted Anomaly Detection Framework. Firstly the number of clusters is not previously known, so choosing $K$ for $K$-means is not an option, and secondly, the distance measure used does not form a metric space. Next section delves deeper into what it means when a measure does not form a metric space. -\section{Clustering on Topological Space}\label{sec:cluster_topo} +\section{Clustering in Topological Space}\label{sec:cluster_topo} This section explains the difference between metric and topological space and clustering approaches for topological spaces. -A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X \to \mathbb{R}$\todo{ad pripominka, ze by to melo byt NxN, takhle to je popsane v te citovane knize. Prijde mi, ze je to obecnejsi a drzim se toho jak to popsal ten matematik.} which satisfies the following conditions: +A metric space, as defined in \cite{choudhary1992elements}, is a pair $(X, d)$ where $X$ is a set and $d$ is a mapping from $X \times X \to \mathbb{R}$ which satisfies the following conditions: \begin{enumerate} \item [(i)] $d(x, y) \geq 0$; \item [(ii)] $d(x, y) = 0 \iff x = y$; @@ -394,7 +395,7 @@ \section{Clustering on Topological Space}\label{sec:cluster_topo} \item [(iv)] $d(x, z) \leq d(x, y) + d(x, z) \mathrm{for} x, y, z \in X$. \end{enumerate} -The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctadf} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms form a subspace for comparing each pair of samples. +The similarity measure that is used in this thesis as explained in Section~\ref{sec:ctadf} does not fulfill the last point of the definition above. Furthermore, it is a pairwise similarity that forms a subspace for comparing each pair of samples. This is why this similarity measure forms a topological space, which is defined according to, e.g., \cite{stahl2014introduction}. Given any set $S$ a topology on $S$ is a family $F ={F_{\alpha} | \alpha \in A}$, where $A$ is some indexing set, each $F_{\alpha} \subseteq S$\todo{ad poznamka proc je to tady vsunute - je to prima citace z te knihy}, and with the following properties: @@ -437,7 +438,7 @@ \section{Representative Selection} If it was possible to represent these clusters in a different way than keeping all track of all of the objects, further operations on these clusters would run faster. A cluster prototype is a data sample that represents all samples in the data cluster. -According to \ref{tan2014introduction}, there are three motivations for finding the most representative cluster prototypes: +According to \cite{tan2014introduction}, there are three motivations for finding the most representative cluster prototypes: \begin{itemize} \item Summarization \item Compression @@ -451,11 +452,11 @@ \subsection{Definition of Representative Selection for Non-Metric Spaces}\label{ Representative selection aims to find a minimal subset of examples from a cluster, that carries sufficient information about the whole cluster. This problem was well defined in \cite{liebman2015representative} and the following definition is taken from that paper. -Let $S$ be a data set, $d : S \times S \to \mathbb{R} +$ be a distance measure (not necessarily a metric), and $\delta$ be a distance threshold below which samples are considered sufficiently similar. -The task is finding a representative subset $Z \subset S$ that best encapsulates the data. +Let $X$ be a data set, $d : X \times X \to \mathbb{R} +$ be a distance measure (not necessarily a metric), and $\delta$ be a distance threshold below which samples are considered sufficiently similar. +The task is finding a representative subset $Z \subseteq S$ that best encapsulates the data. Two following requirements are imposed on an algorithm for finding a representative subset: \begin{itemize} - \item \textbf{Requirement 1}: The algorithm must return a subset $Z \subset S$ such that for any sample $x \in S$, there exists a sample $z \in C$ satisfying $d(x, z) \le \delta$. + \item \textbf{Requirement 1}: The algorithm must return a subset $Z \subseteq S$ such that for any sample $x \in S$, there exists a sample $z \in C$ satisfying $d(x, z) \le \delta$. \item \textbf{Requirement 2} : The algorithm cannot rely on a metric representation of the samples in $S$. \end{itemize} To compare the quality of different subsets returned by different algorithms, two criteria are measured: @@ -490,12 +491,12 @@ \subsection{Representative Selection in a Metric Space}\label{sec:rep_select_met However, if a calculated centroid is not meaningful (see Figure~\ref{img:centroids} b)), a medoid can be selected as an alternative to it. A medoid is a point that is in the set that minimizes the average distance to all the other points in the set. -It can be thought of as a median of the dataset \todo{this is good for an explanation, but not very scientific, as it is not entirely true, how to explain better?}. +It can be thought of as a median of the dataset. Formally, medoid is defined as: \begin{equation} -x_{medoid} = \mathrm{argmin}_{y \in \{x_1, x_2, ..., x_n\}} \sum_{i=1}^{n}{d(y, x_i)}\:, +x_{medoid} = \mathrm{argmin}_{y \in \{x_1, x_2, ..., x_n\}} \sum_{i=1}^{n}{d(y, x_i)}\:, \tag{5} \end{equation} -where $x_1, x_2, ..., x_n$ is a set of $n$ points in a space with a distance function $d$. \todo{definition taken from wikipedia - similar is in xu2011network but not that nice - how to cite?? } +where $x_1, x_2, ..., x_n$ is a set of $n$ points in a space with a distance function $d$. In Figure~\ref{img:medoids} medoids were chosen instead of centroids. \begin{figure} \includegraphics[width=\linewidth]{img/medoids.png} @@ -507,7 +508,8 @@ \subsection{Representative Selection in a Topological Space}\label{sec:rep_selec Clearly, the concept of centroid explained in the previous section cannot be used to solve the problem of finding a representative in an arbitrary space (i.e., topological space). Medoids, of course, can be selected for datasets in arbitrary spaces. -$K$-Medoids algorithm that uses the concept of medoids instead of centroids that are used in the $K$-Means algorithm while retaining its good properties. +$K$-Medoids algorithm resembles $K$-Means algorithm in breaking the dataset into $K$ groups. +In these groups it finds a medoid that is minimizes the distance to each other point in the group. $K$-Medoids is most commonly used for representative selection in topological spaces. There are not many more well-explored methods for solving this problem. @@ -533,12 +535,10 @@ \subsection{Random Selection}\label{subsec:random_select} Random selection algorithm serves as a baseline to measure the improvement as it is the method currently used in Cognitive Targeted Anomaly Detection Framework. It selects a given number of representatives from the cluster randomly. -In clusters dense clusters, selecting random samples from each cluster leads to undesired noise. +In dense clusters, selecting random samples from each cluster leads to undesired noise. However as experimental results presented in \cite{kopp2018community} in the domain of computer networks the density of clusters is not significant. It, of course, depends on each network, but given the best practices in segmentation of networks, the overlaps in clusters are not expected to be great. -For this thesis, the random selection algorithm serves as a baseline in comparing the results to other methods. - \subsection{Greedy Selection}\label{subsec:greedy_select} \todo{obrazky sem a do dalsi sekce} The greedy selection algorithm is a straightforward algorithm that selects a random sample from the set as the first representative. @@ -562,7 +562,7 @@ \subsection{$\delta$-Medoids One-Shot} The idea behind this algorithm is to go through the dataset looking for representatives that differ at least by the given parameter $\delta$ from each previously selected representatives. By taking this approach, it can in one iteration over data find the representatives that are certain to differ by $\delta$ from each other. -The algorithm is shown in Figure~\ref{alg:delta_medoids_one_shot}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. +The algorithm is shown in Algorithm~\ref{alg:delta_medoids_one_shot}, this algorithm is optimized for better memory efficiency as opposed to the version in the cited paper. The main ideas remain the same, only keeping track of clusters that the points are assigned to is removed for this one-shot version. \begin{algorithm} @@ -593,13 +593,13 @@ \subsection{$\delta$-Medoids Full} The full version of the algorithm runs a one-shot algorithm multiple times. Each time it goes through the dataset it selects a better medoid than before. -Once two passes through the data do not change any medoid, the algorithm stops. -This algorithm is shown in Figure~\ref{alg:delta_medoids_full}. +If two consecutive passes through the data do not change any medoid, the algorithm stops. +This algorithm is shown in Algorithm~\ref{alg:delta_medoids_full}. Original algorithm from \cite{liebman2015representative} lacks the routine \textit{ReduceClusters} on line 24. -This routine is introduced in this thesis as an improvement of the algorithm because of the algorithm properties found in Section ~\ref{sec:exp1}. +This routine is introduced in this thesis as an improvement of the algorithm because of the algorithm properties found in Section~\ref{sec:exp1}. This approach is slower, as the main routine from one shot algorithm has to be run multiple times. -Experimental results of \cite{liebman2015representative} show that the algorithm converges in very few (<10) cycles. +Experimental results of \cite{liebman2015representative} show that the algorithm converges in very few ($<$10) cycles. \todo{To samozrejme zalezi na volbe datasetu, jak to jednoduse vysvetlit.} On the other hand, it is able to select better medoids, thus getting rid of the difficult choice of the first representative to select. \begin{algorithm} @@ -640,8 +640,8 @@ \subsection{$\delta$-Medoids Full} \end{algorithmic} \end{algorithm} -The subroutine \textit{ReduceClusters} shown in Figure~\ref{alg:reduce} helps to reduce the number of selected representatives to a number lower than a constant $\kappa$. -If a representative does not cover at least 1\% of the cluster, the routine tries to add him to the coverage of the representative that is similar to it. +The subroutine \textit{ReduceClusters} shown in Algorithm~\ref{alg:reduce} helps to reduce the number of selected representatives. +If a representative does not cover at least 1\% of the cluster, the routine tries to add him to the coverage of the representative that is the most similar. If no such similar representative exists, it is considered noise and the representative is dropped from the cluster. \subsection{$\delta$-Medoids Modified} @@ -689,7 +689,6 @@ \chapter{Datasets}\label{ch:datasets} Below, the datasets used in this thesis are presented. There are three types of datasets used: artificially created, image recognition and real network dataset. Each section corresponds to one of the listed types. -\todo{is this introduction enough? What is missing?} \section{Datasets Created for this Work} @@ -710,16 +709,18 @@ \section{Datasets Created for this Work} \end{figure} Similarity measure used in these datasets was Euclidean distance. -The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter X. \todo{Link to chapter} The Overlap dataset consists of 3 clusters in shapes of blocks. +The Blobs datasets are very easy to represent with a centroid and were tested whether and how much points is needed to represent them using methods explained in Chapter~\ref{ch:theory}. +The Overlap dataset consists of 3 clusters in shapes of blocks. These blocks partly overlap to test how selected algorithms cope with overlapping clusters. -Circles 3D is one of the hardest datasets to represent even by methods that are designed specifically for metric space. +Circles 3D and Moons are one of the hardest datasets to represent even by methods that are designed specifically for metric space. These two datasets were chosen to test the resistance to noise in selecting representatives. -There are two clusters as shown in Figure~\ref{img:datasets}. +All of these datasets are shown in Figure~\ref{img:datasets}. \section{Image Recognition Datasets} -One of the best-documented areas of clustering is the area of image recognition. -That is why two datasets from this area were chosen for measuring the precision and complexity of each method. -Both of them are labeled datasets of features collected from digits written by human hand. +One of the best-documented application domains for clustering is image recognition. +Therefore, two datasets from this area were chosen for measuring the precision and complexity of each method. +The first one is a labeled dataset of features collected from digits written by human hand. +The other one are anotated black and white pictures of clothing and shoes. The Pen-Based Recognition of Handwritten Digits Data Set \cite{dua1998pendigit} consists of 10922 samples with 16 attributes. Each sample represents features collected from a handwritten digit. @@ -727,11 +728,14 @@ \section{Image Recognition Datasets} Features represent coordinate information about the digits as they were written on a $500\times500$ pixel frame. This dataset is split to train dataset (70\%) and test dataset (30\%). -The Multiple Features Data Set \cite{dua1998pendigit} consist of \todo{explain how is it different} \todo{add MNIST Fashion - if works remove multiple features??? Even though I have results.?} +The MNIST Fashion dataset \todo{citation} consist of a training set of 60,000 samples and 10,000 test samples. +A sample from this dataset is a black and hite grayscale image of a piece of clothing or a shoe. +Each sample belongs to one of 10 classes. +This problem is much more complex than handwritten digits and is currently used as a benchmark dataset for classification problems. -\section{Real Network Dataset}\label{sec:real-data} +\section{Network Security Dataset}\label{sec:real-data} Real network traffic captured from a company with approximately 20 thousand employees. -The capture is from 24 hours of a working day a consists of \todo{count flows} flows. +The capture is from 24 hours of a working day and consists of \todo{count flows} flows. Proxy logs were used to capture the connections. Hosts that appeared in that capture were clustered using the method explained in Section~\ref{sec:cluster_topo}. These clusters with all hosts labeled were used as real network dataset in this thesis. @@ -745,17 +749,17 @@ \chapter{Experiments}\label{ch:experiments} For each experiment, the motivation, experimental setup, results and conclusion are listed. All experiments were made in Jupyter Notebook technology using Python 3.7 kernel. -For loading data from files and storing them in memory was used the Pandas library version 0.23.4\todo{link to documentation}. -These technologies were chosen due to this thesis's experimental nature, as the goal was to produce a prototype solution for this problem. +For loading data from files and storing them in memory the Pandas library version 0.23.4 was used.\todo{link to documentation}. -\section{Experiment 1}\label{sec:exp1}\todo{name differently than Experiment?} +%For all experiments below the -The first experiment was run on easily the data specifically created for this thesis. -It served as a sanity check of the proposed methods. -The methods that were explained in the previous chapter\todo{make reference} are designed to work in non-metric space but they still work in a metric space. +\section{Visualizable datasets}\label{sec:exp1} + +The first experiment was run on the data specifically created for this thesis. + +The methods that were explained in the previous chapter are designed to work in non-metric space but they still work in a metric space. Using them on metric data and visualizing the results helped a lot to check the assumptions about results experimentally. -Also, the need for selection of few representatives from each dataset was tested. -It is expected, for these simple clusters, not to exceed 30 selected representatives. +Alsi the ability to select theminimal number of representatives was selected. \subsection{Experimental Setup} Datasets used for this experiment were: @@ -766,8 +770,8 @@ \subsection{Experimental Setup} \item Noisy Circles 3D \end{itemize} -All 4 algorithms were run. -The $\delta$ was estimated as \todo{how is median at lower ten percent called, put it to maths} times 1.05 from distances from one point in the data to all of the others. +All 5 algorithms were run. +The $\delta$ was estimated as 5\% quantile of distances from one random point in the data to all of the others. \subsection{Results} From bf1355095f50e69b865c92dbdc0d7a08068f49c9 Mon Sep 17 00:00:00 2001 From: Jaroslav Hlavac Date: Sun, 12 May 2019 15:44:48 +0200 Subject: [PATCH 56/65] merged pictures and results for exp1 and exp2 --- text/hlavac-thesis.tex | 136 ++++++-------------------------------- text/img/DummyFigure.pdf | Bin 9718 -> 0 bytes text/img/exp1_blobs.png | Bin 23605 -> 57391 bytes text/img/exp1_circles.png | Bin 24215 -> 55448 bytes text/img/exp1_moons.png | Bin 22300 -> 54153 bytes text/img/exp1_overlap.png | Bin 36520 -> 71514 bytes text/img/exp2_blobs.png | Bin 23605 -> 0 bytes text/img/exp2_circles.png | Bin 22544 -> 0 bytes text/img/exp2_moons.png | Bin 21980 -> 0 bytes text/img/exp2_overlap.png | Bin 36125 -> 0 bytes 10 files changed, 19 insertions(+), 117 deletions(-) delete mode 100644 text/img/DummyFigure.pdf delete mode 100644 text/img/exp2_blobs.png delete mode 100644 text/img/exp2_circles.png delete mode 100644 text/img/exp2_moons.png delete mode 100644 text/img/exp2_overlap.png diff --git a/text/hlavac-thesis.tex b/text/hlavac-thesis.tex index 67be0f9..a57789b 100644 --- a/text/hlavac-thesis.tex +++ b/text/hlavac-thesis.tex @@ -780,12 +780,12 @@ \subsection{Results} \textbf{Blobs 3D} \begin{table}[H] -\begin{tabular}{l|lllll} +\begin{tabular}{l|llllll} \hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 400 & 21 & 23 & 23 & 23 \\ -B & 400 & 17 & 20 & 20 & 20 \\ -C & 400 & 19 & 20 & 20 &20 \\ \hline + \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 400 & 21 & 23 & 23 & 23 & 23 \\ +B & 400 & 17 & 20 & 20 & 20 & 20 \\ +C & 400 & 19 & 20 & 20 & 20 & 20 \\ \hline \end{tabular} \caption{Number of selected representatives for Blobs 3D dataset} \end{table} @@ -794,17 +794,17 @@ \subsection{Results} \includegraphics[width=\linewidth]{img/exp1_blobs.png} \caption{Confusion matrices for Blobs 3D dataset} \label{img:exp1_blobs} -\end{figure} \todo{generate pictures with cluster names rathern then numbers, or do i have to?} +\end{figure} \textbf{Overlap} \begin{table}[H] -\begin{tabular}{l|lllll} +\begin{tabular}{l|llllll} \hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 800 & 25 & 23 & 23 & 23 \\ -B & 960 & 26 & 21 & 21 & 21 \\ -C & 640 & 22 & 24 & 24 & 24 \\ \hline + \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 800 & 25 & 23 & 23 & 23 & 23 \\ +B & 960 & 26 & 21 & 23 & 21 & 21 \\ +C & 640 & 22 & 24 & 24 & 24 & 24 \\ \hline \end{tabular} \caption{Number of selected representatives for Overlap dataset} \end{table} @@ -818,11 +818,11 @@ \subsection{Results} \textbf{Moons} \begin{table}[H] -\begin{tabular}{l|lllll} +\begin{tabular}{l|llllll} \hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 600 & 18 & 19 & 19 & 19 \\ -B & 600 & 19 & 18 & 18 & 18 \\ \hline + \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 600 & 22 & 21 & 22 & 21 & 21 \\ +B & 600 & 21 & 24 & 25 & 24 & 24 \\ \hline \end{tabular} \caption{Number of selected representatives for Moons dataset} \end{table} @@ -836,11 +836,11 @@ \subsection{Results} \textbf{Circles} \begin{table}[H] -\begin{tabular}{l|lllll} +\begin{tabular}{l|llllll} \hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 600 & 14 & 14 & 14 & 14 \\ -B & 600 & 8 & 7 & 7 & 7 \\ \hline + \textbf{Cluster} & \specialcell{\textbf{Training}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Modified}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline +A & 600 & 15 & 13 & 13 & 13 & 13 \\ +B & 600 & 6 & 8 & 8 & 8 & 8 \\ \hline \end{tabular} \caption{Number of selected representatives for Circles dataset} \end{table} @@ -866,104 +866,6 @@ \subsection{Conclusion} Selecting the border samples clearly was not intended and the $\delta$-Medoids algorithm had to select only the relevant samples. The relevance of a representative was determined to the number of points that fit into its $\delta$-neighborhood. \todo{where to list the changes, here or in the theoretical chapter, theory better I think} -\section{Experiment 2}\label{sec:exp2} - -This experiment was run to test the changes made in the $\delta$-Medoids algorithm. -It was updated not to select the border samples, as is explained in section\todo{ref the section where I explain it}. - -\subsection{Experimental Setup} -Datasets used for this experiment were: -\begin{itemize} - \item Blobs 3D - \item Overlap - \item Moons - \item Noisy Circles 3D -\end{itemize} - -All 4 algorithms were run. -The $\delta$ was estimated as \todo{how is median at lower ten percent called, put it to maths} times 1.05 from distances from one point in the data to all of the others. - -\subsection{Results} - -The results for different methods are listed below. - -\textbf{Blobs 3D} - -\begin{table}[H] -\begin{tabular}{l|lllll} -\hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 400 & 23 & 24 & 24 & 24 \\ -B & 400 & 21 & 22 & 22 & 22 \\ -C & 400 & 20 & 21 & 21 & 21 \\ \hline -\end{tabular} -\caption{Number of selected representatives for Blobs 3D dataset} -\end{table} - -\begin{figure}[H] - \includegraphics[width=\linewidth]{img/exp2_blobs.png} - \caption{Confusion matrices for Blobs 3D dataset} - \label{img:exp2_blobs} -\end{figure} - -\textbf{Overlap} - -\begin{table}[H] -\begin{tabular}{l|lllll} -\hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 800 & 22 & 21 & 21 & 21 \\ -B & 960 & 21 & 20 & 20 & 20 \\ -C & 640 & 19 & 19 & 19 & 19 \\ \hline -\end{tabular} -\caption{Number of selected representatives for Overlap dataset} -\end{table} - -\begin{figure}[H] - \includegraphics[width=\linewidth]{img/exp2_overlap.png} - \caption{Confusion matrices for Overlap dataset} - \label{img:exp2_overlap} -\end{figure} - -\textbf{Moons} - -\begin{table}[H] -\begin{tabular}{l|lllll} -\hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 600 & 21 & 18 & 18 & 18 \\ -B & 600 & 16 & 20 & 20 & 20 \\ \hline -\end{tabular} -\caption{Number of selected representatives for Moons dataset} -\end{table} - -\begin{figure}[H] - \includegraphics[width=\linewidth]{img/exp2_moons.png} - \caption{Confusion matrices for Moons dataset} - \label{img:exp2_moons} -\end{figure} - -\textbf{Circles} - -\begin{table}[H] -\begin{tabular}{l|lllll} -\hline - \textbf{Cluster} & \specialcell{\textbf{All}\\ \textbf{Samples}} & \specialcell{\textbf{Greedy}\\ \textbf{Selection}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{One-Shot}} & \specialcell{\textbf{$\delta$-Medoids}\\ \textbf{Full}} & \specialcell{\textbf{Random}\\ \textbf{Selection}} \\ \hline -A & 600 & 15 & 15 & 15 & 15 \\ -B & 600 & 8 & 8 & 8 & 8 \\ \hline -\end{tabular} -\caption{Number of selected representatives for Circles dataset} -\end{table} - -\begin{figure}[H] - \includegraphics[width=\linewidth]{img/exp2_circles.png} - \caption{Confusion matrices for Circles dataset} - \label{img:exp2_circles} -\end{figure} - - -\subsection{Conclusion} - The results of this experiment empirically supported the expected result of not selecting the border samples from a dataset as can be seen on Figure~\ref{img:blobs_good_select} \begin{figure}[H] \includegraphics[width=\linewidth]{img/delta_medoids_select_better.png} diff --git a/text/img/DummyFigure.pdf b/text/img/DummyFigure.pdf deleted file mode 100644 index 4247749bcb6a5bbe8aa6bb8644bd7ee194aa04c7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 9718 zcmeHtX*kqt`2P%H5{hs_nMo*>Sq(E2vM*&P+mwu%!7vLm!;F2)T1fUSYqpTBqEMnz zNm>qRk7!k#6h-x$NvY29KmT+6uJhve;&|cu&a-^(`}y3T=brC#&!b{vWTK9MqQzB4 zMz7rz7e&A!a0tW0M_gMQWY+e?tz)pNn|dbw{V08 z|AQw3g5~*V2eTm4z1ZFmG@9>BAb*`&Fgpf=4M8Bj_S^HAfgm+t4nclon6n+(6Xt|R zK#&jwj{z2&NhZ+5MZF(*c#u}PyL-6*0hAI8*NbkH?CB8pSPooXFAJG^3>93xq1)o1 zl&}eVWcz6Sb^uTqaN9;?TwIjL;5UqKT(Z~%CY#G7dy0#~#YI(AjIB+?MZfVYE{a6` zpmZs&AQ&xP1hsf!Tv(JQ8fFhcVSaJc-QAt+?k+_J2&`8Vb~p>_*($bHyw{`Frtj=P z@7eCNYc8vurv?COoJW-%>=m3h#C!f?&Qre4Zw`Q*p% zWh?jG%x%ifER9J{?0=+k#VFFkZvE!WyN`SvAGsFZ z*Swbs)I|j<<#L~?<56agA z%?k{aZqUr6PuWUN=2*m^P1g>dIA6=%RL?9UH}4)jcge&l2(NW{Zc`zy^P`TKp31Gk 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