Dynamic_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB, BernoulliNB\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import cohen_kappa_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "qWH5TaKRC_tB"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv(\"/content/dynamic_api_call_sequence_per_malware_100_0_306.csv\")\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "8QXhSLi1Dejs",
        "outputId": "0dc13bbf-4b0f-4d96-defe-19bab367b711"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               hash  t_0  t_1  t_2  t_3  t_4  t_5  t_6  t_7  \\\n",
              "0  071e8c3f8922e186e57548cd4c703a5d  112  274  158  215  274  158  215  298   \n",
              "1  33f8e6d08a6aae939f25a8e0d63dd523   82  208  187  208  172  117  172  117   \n",
              "2  b68abd064e975e1c6d5f25e748663076   16  110  240  117  240  117  240  117   \n",
              "3  72049be7bd30ea61297ea624ae198067   82  208  187  208  172  117  172  117   \n",
              "4  c9b3700a77facf29172f32df6bc77f48   82  240  117  240  117  240  117  240   \n",
              "\n",
              "   t_8  ...  t_91  t_92  t_93  t_94  t_95  t_96  t_97  t_98  t_99  malware  \n",
              "0   76  ...    71   297   135   171   215    35   208    56    71        1  \n",
              "1  172  ...    81   240   117    71   297   135   171   215    35        1  \n",
              "2  240  ...    65   112   123    65   112   123    65   113   112        1  \n",
              "3  172  ...   208   302   208   302   187   208   302   228   302        1  \n",
              "4  117  ...   209   260    40   209   260   141   260   141   260        1  \n",
              "\n",
              "[5 rows x 102 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd789b6d-5672-4095-9d77-5f8724c4735f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hash</th>\n",
              "      <th>t_0</th>\n",
              "      <th>t_1</th>\n",
              "      <th>t_2</th>\n",
              "      <th>t_3</th>\n",
              "      <th>t_4</th>\n",
              "      <th>t_5</th>\n",
              "      <th>t_6</th>\n",
              "      <th>t_7</th>\n",
              "      <th>t_8</th>\n",
              "      <th>...</th>\n",
              "      <th>t_91</th>\n",
              "      <th>t_92</th>\n",
              "      <th>t_93</th>\n",
              "      <th>t_94</th>\n",
              "      <th>t_95</th>\n",
              "      <th>t_96</th>\n",
              "      <th>t_97</th>\n",
              "      <th>t_98</th>\n",
              "      <th>t_99</th>\n",
              "      <th>malware</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>071e8c3f8922e186e57548cd4c703a5d</td>\n",
              "      <td>112</td>\n",
              "      <td>274</td>\n",
              "      <td>158</td>\n",
              "      <td>215</td>\n",
              "      <td>274</td>\n",
              "      <td>158</td>\n",
              "      <td>215</td>\n",
              "      <td>298</td>\n",
              "      <td>76</td>\n",
              "      <td>...</td>\n",
              "      <td>71</td>\n",
              "      <td>297</td>\n",
              "      <td>135</td>\n",
              "      <td>171</td>\n",
              "      <td>215</td>\n",
              "      <td>35</td>\n",
              "      <td>208</td>\n",
              "      <td>56</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33f8e6d08a6aae939f25a8e0d63dd523</td>\n",
              "      <td>82</td>\n",
              "      <td>208</td>\n",
              "      <td>187</td>\n",
              "      <td>208</td>\n",
              "      <td>172</td>\n",
              "      <td>117</td>\n",
              "      <td>172</td>\n",
              "      <td>117</td>\n",
              "      <td>172</td>\n",
              "      <td>...</td>\n",
              "      <td>81</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>71</td>\n",
              "      <td>297</td>\n",
              "      <td>135</td>\n",
              "      <td>171</td>\n",
              "      <td>215</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b68abd064e975e1c6d5f25e748663076</td>\n",
              "      <td>16</td>\n",
              "      <td>110</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>...</td>\n",
              "      <td>65</td>\n",
              "      <td>112</td>\n",
              "      <td>123</td>\n",
              "      <td>65</td>\n",
              "      <td>112</td>\n",
              "      <td>123</td>\n",
              "      <td>65</td>\n",
              "      <td>113</td>\n",
              "      <td>112</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>72049be7bd30ea61297ea624ae198067</td>\n",
              "      <td>82</td>\n",
              "      <td>208</td>\n",
              "      <td>187</td>\n",
              "      <td>208</td>\n",
              "      <td>172</td>\n",
              "      <td>117</td>\n",
              "      <td>172</td>\n",
              "      <td>117</td>\n",
              "      <td>172</td>\n",
              "      <td>...</td>\n",
              "      <td>208</td>\n",
              "      <td>302</td>\n",
              "      <td>208</td>\n",
              "      <td>302</td>\n",
              "      <td>187</td>\n",
              "      <td>208</td>\n",
              "      <td>302</td>\n",
              "      <td>228</td>\n",
              "      <td>302</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>c9b3700a77facf29172f32df6bc77f48</td>\n",
              "      <td>82</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>...</td>\n",
              "      <td>209</td>\n",
              "      <td>260</td>\n",
              "      <td>40</td>\n",
              "      <td>209</td>\n",
              "      <td>260</td>\n",
              "      <td>141</td>\n",
              "      <td>260</td>\n",
              "      <td>141</td>\n",
              "      <td>260</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 102 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd789b6d-5672-4095-9d77-5f8724c4735f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd789b6d-5672-4095-9d77-5f8724c4735f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bd789b6d-5672-4095-9d77-5f8724c4735f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALLzRuayBGTY",
        "outputId": "ff12d87a-6bdc-4405-c2bd-76776843ffe5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmHP8diXBJAx",
        "outputId": "9d882f4e-f01b-4584-851a-f93678c4afab"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['hash', 't_0', 't_1', 't_2', 't_3', 't_4', 't_5', 't_6', 't_7', 't_8',\n",
              "       ...\n",
              "       't_91', 't_92', 't_93', 't_94', 't_95', 't_96', 't_97', 't_98', 't_99',\n",
              "       'malware'],\n",
              "      dtype='object', length=102)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ai5DLzh0BLK7",
        "outputId": "dc971c87-7a83-4b45-ad9c-c356fe4616cf"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hash       0\n",
              "t_0        0\n",
              "t_1        0\n",
              "t_2        0\n",
              "t_3        0\n",
              "          ..\n",
              "t_96       0\n",
              "t_97       0\n",
              "t_98       0\n",
              "t_99       0\n",
              "malware    0\n",
              "Length: 102, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dtypes.astype(\"str\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDZImIPVBMwE",
        "outputId": "60eb5cd5-5e16-4652-96d4-b1b3d2f44f44"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hash       object\n",
              "t_0         int64\n",
              "t_1         int64\n",
              "t_2         int64\n",
              "t_3         int64\n",
              "            ...  \n",
              "t_96        int64\n",
              "t_97        int64\n",
              "t_98        int64\n",
              "t_99        int64\n",
              "malware     int64\n",
              "Length: 102, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "-kliZWwfBOqV",
        "outputId": "82b9ef68-6701-44d1-e0f6-bffd8e6678d8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               hash  t_0  t_1  t_2  t_3  t_4  t_5  t_6  t_7  \\\n",
              "0  071e8c3f8922e186e57548cd4c703a5d  112  274  158  215  274  158  215  298   \n",
              "1  33f8e6d08a6aae939f25a8e0d63dd523   82  208  187  208  172  117  172  117   \n",
              "2  b68abd064e975e1c6d5f25e748663076   16  110  240  117  240  117  240  117   \n",
              "3  72049be7bd30ea61297ea624ae198067   82  208  187  208  172  117  172  117   \n",
              "4  c9b3700a77facf29172f32df6bc77f48   82  240  117  240  117  240  117  240   \n",
              "\n",
              "   t_8  ...  t_91  t_92  t_93  t_94  t_95  t_96  t_97  t_98  t_99  malware  \n",
              "0   76  ...    71   297   135   171   215    35   208    56    71        1  \n",
              "1  172  ...    81   240   117    71   297   135   171   215    35        1  \n",
              "2  240  ...    65   112   123    65   112   123    65   113   112        1  \n",
              "3  172  ...   208   302   208   302   187   208   302   228   302        1  \n",
              "4  117  ...   209   260    40   209   260   141   260   141   260        1  \n",
              "\n",
              "[5 rows x 102 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aad0140d-f54b-4b77-9b99-e473546b627e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>hash</th>\n",
              "      <th>t_0</th>\n",
              "      <th>t_1</th>\n",
              "      <th>t_2</th>\n",
              "      <th>t_3</th>\n",
              "      <th>t_4</th>\n",
              "      <th>t_5</th>\n",
              "      <th>t_6</th>\n",
              "      <th>t_7</th>\n",
              "      <th>t_8</th>\n",
              "      <th>...</th>\n",
              "      <th>t_91</th>\n",
              "      <th>t_92</th>\n",
              "      <th>t_93</th>\n",
              "      <th>t_94</th>\n",
              "      <th>t_95</th>\n",
              "      <th>t_96</th>\n",
              "      <th>t_97</th>\n",
              "      <th>t_98</th>\n",
              "      <th>t_99</th>\n",
              "      <th>malware</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>071e8c3f8922e186e57548cd4c703a5d</td>\n",
              "      <td>112</td>\n",
              "      <td>274</td>\n",
              "      <td>158</td>\n",
              "      <td>215</td>\n",
              "      <td>274</td>\n",
              "      <td>158</td>\n",
              "      <td>215</td>\n",
              "      <td>298</td>\n",
              "      <td>76</td>\n",
              "      <td>...</td>\n",
              "      <td>71</td>\n",
              "      <td>297</td>\n",
              "      <td>135</td>\n",
              "      <td>171</td>\n",
              "      <td>215</td>\n",
              "      <td>35</td>\n",
              "      <td>208</td>\n",
              "      <td>56</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33f8e6d08a6aae939f25a8e0d63dd523</td>\n",
              "      <td>82</td>\n",
              "      <td>208</td>\n",
              "      <td>187</td>\n",
              "      <td>208</td>\n",
              "      <td>172</td>\n",
              "      <td>117</td>\n",
              "      <td>172</td>\n",
              "      <td>117</td>\n",
              "      <td>172</td>\n",
              "      <td>...</td>\n",
              "      <td>81</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>71</td>\n",
              "      <td>297</td>\n",
              "      <td>135</td>\n",
              "      <td>171</td>\n",
              "      <td>215</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b68abd064e975e1c6d5f25e748663076</td>\n",
              "      <td>16</td>\n",
              "      <td>110</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>...</td>\n",
              "      <td>65</td>\n",
              "      <td>112</td>\n",
              "      <td>123</td>\n",
              "      <td>65</td>\n",
              "      <td>112</td>\n",
              "      <td>123</td>\n",
              "      <td>65</td>\n",
              "      <td>113</td>\n",
              "      <td>112</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>72049be7bd30ea61297ea624ae198067</td>\n",
              "      <td>82</td>\n",
              "      <td>208</td>\n",
              "      <td>187</td>\n",
              "      <td>208</td>\n",
              "      <td>172</td>\n",
              "      <td>117</td>\n",
              "      <td>172</td>\n",
              "      <td>117</td>\n",
              "      <td>172</td>\n",
              "      <td>...</td>\n",
              "      <td>208</td>\n",
              "      <td>302</td>\n",
              "      <td>208</td>\n",
              "      <td>302</td>\n",
              "      <td>187</td>\n",
              "      <td>208</td>\n",
              "      <td>302</td>\n",
              "      <td>228</td>\n",
              "      <td>302</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>c9b3700a77facf29172f32df6bc77f48</td>\n",
              "      <td>82</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>240</td>\n",
              "      <td>117</td>\n",
              "      <td>...</td>\n",
              "      <td>209</td>\n",
              "      <td>260</td>\n",
              "      <td>40</td>\n",
              "      <td>209</td>\n",
              "      <td>260</td>\n",
              "      <td>141</td>\n",
              "      <td>260</td>\n",
              "      <td>141</td>\n",
              "      <td>260</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 102 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aad0140d-f54b-4b77-9b99-e473546b627e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aad0140d-f54b-4b77-9b99-e473546b627e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aad0140d-f54b-4b77-9b99-e473546b627e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, 1:101], data['malware'], test_size=0.20, random_state=42)"
      ],
      "metadata": {
        "id": "_PqG0RmTBQQf"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gaussian Naive Bayes Model\n"
      ],
      "metadata": {
        "id": "Gxvwk-DVBXjv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gnb = GaussianNB()\n",
        "gnb.fit(X_train, y_train)\n",
        "\n",
        "# pred\n",
        "pred = gnb.predict(X_test)\n",
        "\n",
        "# accuracy\n",
        "accuracy = accuracy_score(pred, y_test)\n",
        "print(\"naive_bayes\")\n",
        "print(accuracy)\n",
        "print(classification_report(pred, y_test, labels=None))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IticnjN3BS5m",
        "outputId": "24449f4e-58a5-4a80-8734-22cc0389efa6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "naive_bayes\n",
            "0.8893573381950775\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.46      0.11      0.18       951\n",
            "           1       0.90      0.98      0.94      7825\n",
            "\n",
            "    accuracy                           0.89      8776\n",
            "   macro avg       0.68      0.55      0.56      8776\n",
            "weighted avg       0.85      0.89      0.86      8776\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "K Neighbours Model"
      ],
      "metadata": {
        "id": "ut_go0eSBc1Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(3,15,3):\n",
        "    \n",
        "    neigh = KNeighborsClassifier(n_neighbors=i)\n",
        "    neigh.fit(X_train, y_train)\n",
        "    pred = neigh.predict(X_test)\n",
        "    # accuracy\n",
        "    accuracy = accuracy_score(pred, y_test)\n",
        "    print(\"kneighbors {}\".format(i))\n",
        "    print(accuracy)\n",
        "    print(classification_report(pred, y_test, labels=None))\n",
        "    print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2CK5G7bBhaV",
        "outputId": "400a5976-8a3e-4aec-fd1b-0e1883a97fdb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kneighbors 3\n",
            "0.9843892433910666\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.45      0.89      0.60       113\n",
            "           1       1.00      0.99      0.99      8663\n",
            "\n",
            "    accuracy                           0.98      8776\n",
            "   macro avg       0.72      0.94      0.79      8776\n",
            "weighted avg       0.99      0.98      0.99      8776\n",
            "\n",
            "\n",
            "kneighbors 6\n",
            "0.9847310847766636\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.93      0.60       108\n",
            "           1       1.00      0.99      0.99      8668\n",
            "\n",
            "    accuracy                           0.98      8776\n",
            "   macro avg       0.72      0.96      0.80      8776\n",
            "weighted avg       0.99      0.98      0.99      8776\n",
            "\n",
            "\n",
            "kneighbors 9\n",
            "0.9846171376481313\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.93      0.59       107\n",
            "           1       1.00      0.99      0.99      8669\n",
            "\n",
            "    accuracy                           0.98      8776\n",
            "   macro avg       0.72      0.96      0.79      8776\n",
            "weighted avg       0.99      0.98      0.99      8776\n",
            "\n",
            "\n",
            "kneighbors 12\n",
            "0.9841613491340018\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.42      0.93      0.57       101\n",
            "           1       1.00      0.98      0.99      8675\n",
            "\n",
            "    accuracy                           0.98      8776\n",
            "   macro avg       0.71      0.96      0.78      8776\n",
            "weighted avg       0.99      0.98      0.99      8776\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision Tree Model"
      ],
      "metadata": {
        "id": "KxyP6iz4Bnky"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(3,15,3):\n",
        "    \n",
        "    neigh = KNeighborsClassifier(n_neighbors=i)\n",
        "    neigh.fit(X_train, y_train)\n",
        "    pred = neigh.predict(X_test)\n",
        "    # accuracy\n",
        "    accuracy = accuracy_score(pred, y_test)\n",
        "    print(\"kneighbors {}\".format(i))\n",
        "    print(accuracy)\n",
        "    print(classification_report(pred, y_test, labels=None))\n",
        "    print(\"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvRgBYgFBkyI",
        "outputId": "6de0a21a-8408-41c1-a30f-e62109c64167"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kneighbors 3\n",
            "0.9843892433910666\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.45      0.89      0.60       113\n",
            "           1       1.00      0.99      0.99      8663\n",
            "\n",
            "    accuracy                           0.98      8776\n",
            "   macro avg       0.72      0.94      0.79      8776\n",
            "weighted avg       0.99      0.98      0.99      8776\n",
            "\n",
            "\n",
            "kneighbors 6\n",
            "0.9847310847766636\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.93      0.60       108\n",
            "           1       1.00      0.99      0.99      8668\n",
            "\n",
            "    accuracy                           0.98      8776\n",
            "   macro avg       0.72      0.96      0.80      8776\n",
            "weighted avg       0.99      0.98      0.99      8776\n",
            "\n",
            "\n",
            "kneighbors 9\n",
            "0.9846171376481313\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.44      0.93      0.59       107\n",
            "           1       1.00      0.99      0.99      8669\n",
            "\n",
            "    accuracy                           0.98      8776\n",
            "   macro avg       0.72      0.96      0.79      8776\n",
            "weighted avg       0.99      0.98      0.99      8776\n",
            "\n",
            "\n",
            "kneighbors 12\n",
            "0.9841613491340018\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.42      0.93      0.57       101\n",
            "           1       1.00      0.98      0.99      8675\n",
            "\n",
            "    accuracy                           0.98      8776\n",
            "   macro avg       0.71      0.96      0.78      8776\n",
            "weighted avg       0.99      0.98      0.99      8776\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Hv0_VPdxDOCP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}