ch_11/3-EDA_labeled_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis with labeled data\n",
    "Now that we have the labels for our data, we can do some initial EDA to see if there is something different between the hackers and the valid users.\n",
    "\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>source_ip</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_floor</th>\n",
       "      <th>end_ceil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02 02:31:43.326264</td>\n",
       "      <td>2018-01-02 02:35:16.326264</td>\n",
       "      <td>102.139.159.128</td>\n",
       "      <td>0 days 00:03:33</td>\n",
       "      <td>2018-01-02 02:31:00</td>\n",
       "      <td>2018-01-02 02:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02 20:14:02.279476</td>\n",
       "      <td>2018-01-02 20:14:28.279476</td>\n",
       "      <td>119.218.239.234</td>\n",
       "      <td>0 days 00:00:26</td>\n",
       "      <td>2018-01-02 20:14:00</td>\n",
       "      <td>2018-01-02 20:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03 01:25:48.667114</td>\n",
       "      <td>2018-01-03 01:29:13.667114</td>\n",
       "      <td>151.93.164.203</td>\n",
       "      <td>0 days 00:03:25</td>\n",
       "      <td>2018-01-03 01:25:00</td>\n",
       "      <td>2018-01-03 01:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-08 21:41:43.985324</td>\n",
       "      <td>2018-01-08 21:45:56.985324</td>\n",
       "      <td>226.98.192.152</td>\n",
       "      <td>0 days 00:04:13</td>\n",
       "      <td>2018-01-08 21:41:00</td>\n",
       "      <td>2018-01-08 21:46:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-11 17:38:30.974748</td>\n",
       "      <td>2018-01-11 17:42:33.974748</td>\n",
       "      <td>23.81.78.129</td>\n",
       "      <td>0 days 00:04:03</td>\n",
       "      <td>2018-01-11 17:38:00</td>\n",
       "      <td>2018-01-11 17:43:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       start                        end        source_ip  \\\n",
       "0 2018-01-02 02:31:43.326264 2018-01-02 02:35:16.326264  102.139.159.128   \n",
       "1 2018-01-02 20:14:02.279476 2018-01-02 20:14:28.279476  119.218.239.234   \n",
       "2 2018-01-03 01:25:48.667114 2018-01-03 01:29:13.667114   151.93.164.203   \n",
       "3 2018-01-08 21:41:43.985324 2018-01-08 21:45:56.985324   226.98.192.152   \n",
       "4 2018-01-11 17:38:30.974748 2018-01-11 17:42:33.974748     23.81.78.129   \n",
       "\n",
       "         duration         start_floor            end_ceil  \n",
       "0 0 days 00:03:33 2018-01-02 02:31:00 2018-01-02 02:36:00  \n",
       "1 0 days 00:00:26 2018-01-02 20:14:00 2018-01-02 20:15:00  \n",
       "2 0 days 00:03:25 2018-01-03 01:25:00 2018-01-03 01:30:00  \n",
       "3 0 days 00:04:13 2018-01-08 21:41:00 2018-01-08 21:46:00  \n",
       "4 0 days 00:04:03 2018-01-11 17:38:00 2018-01-11 17:43:00  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sqlite3\n",
    "\n",
    "with sqlite3.connect('logs/logs.db') as conn:\n",
    "    logs_2018 = pd.read_sql(\n",
    "        'SELECT * FROM logs WHERE datetime BETWEEN \"2018-01-01\" AND \"2019-01-01\";', \n",
    "        conn, parse_dates=['datetime'], index_col='datetime'\n",
    "    )\n",
    "    hackers_2018 = pd.read_sql(\n",
    "        'SELECT * FROM attacks WHERE start BETWEEN \"2018-01-01\" AND \"2019-01-01\";', \n",
    "        conn, parse_dates=['start', 'end']\n",
    "    ).assign(\n",
    "        duration=lambda x: x.end - x.start, \n",
    "        start_floor=lambda x: x.start.dt.floor('min'),\n",
    "        end_ceil=lambda x: x.end.dt.ceil('min')\n",
    "    )\n",
    "hackers_2018.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function will tell us if the datetimes had hacker activity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_if_hacker(datetimes, hackers, resolution='1min'):\n",
    "    \"\"\"\n",
    "    Check whether a hacker attempted a log in during that time.\n",
    "    \n",
    "    Parameters:\n",
    "        - datetimes: The datetimes to check for hackers\n",
    "        - hackers: The dataframe indicating when the attacks started and stopped\n",
    "        - resolution: The granularity of the datetime. Default is 1 minute.\n",
    "        \n",
    "    Returns:\n",
    "        `pandas.Series` of Booleans.\n",
    "    \"\"\"\n",
    "    date_ranges = hackers.apply(\n",
    "        lambda x: pd.date_range(x.start_floor, x.end_ceil, freq=resolution), \n",
    "        axis=1\n",
    "    )\n",
    "    dates = pd.Series(dtype='object')\n",
    "    for date_range in date_ranges:\n",
    "        dates = pd.concat([dates, date_range.to_series()])\n",
    "    return datetimes.isin(dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's label our data for Q1 so we can look for a separation boundary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>usernames_with_failures</th>\n",
       "      <th>failures</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:08:00</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:09:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:10:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:11:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:12:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     usernames_with_failures  failures   flag\n",
       "datetime                                                     \n",
       "2018-01-01 00:08:00                        1         1  False\n",
       "2018-01-01 00:09:00                        0         0  False\n",
       "2018-01-01 00:10:00                        0         0  False\n",
       "2018-01-01 00:11:00                        0         0  False\n",
       "2018-01-01 00:12:00                        0         0  False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_with_failures = logs_2018.loc['2018-Q1'].assign(\n",
    "    failures=lambda x:  1 - x.success\n",
    ").query('failures > 0').resample('1min').agg(\n",
    "    {'username':'nunique', 'failures': 'sum'}\n",
    ").dropna().rename(\n",
    "    columns={'username':'usernames_with_failures'}\n",
    ")\n",
    "labels = check_if_hacker(users_with_failures.reset_index().datetime, hackers_2018)\n",
    "users_with_failures['flag'] = labels[:users_with_failures.shape[0]].values\n",
    "users_with_failures.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have the labels, we can draw a sample boundary that would separate most of the hackers from the valid users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Usernames with failures on minute resolution')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(\n",
    "    x=users_with_failures.usernames_with_failures, \n",
    "    y=users_with_failures.failures, \n",
    "    alpha=0.25,\n",
    "    hue=users_with_failures.flag\n",
    ")\n",
    "plt.ylim(-4, None)\n",
    "ax.plot([-2, 5], [15, -2], 'r--', label='sample boundary')\n",
    "# sort the legend entries\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))\n",
    "ax.legend(handles, labels, title='flag')\n",
    "plt.title('Usernames with failures on minute resolution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<div style=\"overflow: hidden; margin-bottom: 10px;\">\n",
    "    <div style=\"float: left;\">\n",
    "        <a href=\"./2-unsupervised_anomaly_detection.ipynb\">\n",
    "            <button>&#8592; Previous Notebook</button>\n",
    "        </a>\n",
    "    </div>\n",
    "    <div style=\"float: right;\">\n",
    "        <a href=\"./4-supervised_anomaly_detection.ipynb\">\n",
    "            <button>Next Notebook &#8594;</button>\n",
    "        </a>\n",
    "    </div>\n",
    "</div>\n",
    "<hr>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}