Day4_cybersecurity.ipynb

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    {
      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/vishymails/cyber_security_AIML/blob/main/Day4_cybersecurity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "qf1TL7OTkoNs"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('dataset_malwares.csv')\n"
      ],
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        "id": "ku7Ulj0hopQh"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "data.shape"
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        },
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        "outputId": "b1865080-5073-41b1-bca2-bd6f29859745"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19611, 79)"
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          },
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          "execution_count": 3
        }
      ]
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    {
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      "source": [
        "data.describe()"
      ],
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        "id": "-o3_9KEboxH_",
        "outputId": "7afba28c-a8e9-4066-cae0-5ea6f09b8524"
      },
      "execution_count": 4,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "       e_magic        e_cblp          e_cp        e_crlc     e_cparhdr  \\\n",
              "count  19611.0  19611.000000  19611.000000  19611.000000  19611.000000   \n",
              "mean   23117.0    178.615726     71.660752     49.146958     37.370710   \n",
              "std        0.0    987.200729   1445.192977   1212.201919    864.515405   \n",
              "min    23117.0      0.000000      0.000000      0.000000      0.000000   \n",
              "25%    23117.0    144.000000      3.000000      0.000000      4.000000   \n",
              "50%    23117.0    144.000000      3.000000      0.000000      4.000000   \n",
              "75%    23117.0    144.000000      3.000000      0.000000      4.000000   \n",
              "max    23117.0  59448.000000  63200.000000  64613.000000  43690.000000   \n",
              "\n",
              "         e_minalloc    e_maxalloc          e_ss         e_sp        e_csum  \\\n",
              "count  19611.000000  19611.000000  19611.000000  19611.00000  19611.000000   \n",
              "mean      37.032635  64178.739687     10.418490    226.46530     29.689103   \n",
              "std      915.833139   9110.755873    637.116265   1249.68033   1015.303419   \n",
              "min        0.000000      0.000000      0.000000      0.00000      0.000000   \n",
              "25%        0.000000  65535.000000      0.000000    184.00000      0.000000   \n",
              "50%        0.000000  65535.000000      0.000000    184.00000      0.000000   \n",
              "75%        0.000000  65535.000000      0.000000    184.00000      0.000000   \n",
              "max    43690.000000  65535.000000  61436.000000  65464.00000  63262.000000   \n",
              "\n",
              "       ...  SectionMaxChar  SectionMainChar  DirectoryEntryImport  \\\n",
              "count  ...    1.961100e+04          19611.0          19611.000000   \n",
              "mean   ...    3.163632e+09              0.0              6.112437   \n",
              "std    ...    5.860332e+08              0.0              7.525158   \n",
              "min    ...    1.073742e+09              0.0              0.000000   \n",
              "25%    ...    3.221226e+09              0.0              2.000000   \n",
              "50%    ...    3.221226e+09              0.0              4.000000   \n",
              "75%    ...    3.221226e+09              0.0              8.000000   \n",
              "max    ...    4.294967e+09              0.0            588.000000   \n",
              "\n",
              "       DirectoryEntryImportSize  DirectoryEntryExport  \\\n",
              "count              19611.000000          19611.000000   \n",
              "mean                 101.912804             14.131865   \n",
              "std                  127.076767            154.958102   \n",
              "min                    0.000000              0.000000   \n",
              "25%                   17.000000              0.000000   \n",
              "50%                   80.000000              0.000000   \n",
              "75%                  135.000000              0.000000   \n",
              "max                 4016.000000           7319.000000   \n",
              "\n",
              "       ImageDirectoryEntryExport  ImageDirectoryEntryImport  \\\n",
              "count               1.961100e+04               1.961100e+04   \n",
              "mean                3.368566e+05               4.047213e+05   \n",
              "std                 2.181191e+07               4.704601e+06   \n",
              "min                 0.000000e+00               0.000000e+00   \n",
              "25%                 0.000000e+00               2.960400e+04   \n",
              "50%                 0.000000e+00               8.222400e+04   \n",
              "75%                 0.000000e+00               2.477080e+05   \n",
              "max                 2.147484e+09               5.368914e+08   \n",
              "\n",
              "       ImageDirectoryEntryResource  ImageDirectoryEntryException  \\\n",
              "count                 1.961100e+04                  1.961100e+04   \n",
              "mean                  5.555810e+05                  1.238834e+06   \n",
              "std                   6.772167e+06                  5.868961e+07   \n",
              "min                   0.000000e+00                  0.000000e+00   \n",
              "25%                   4.096000e+04                  0.000000e+00   \n",
              "50%                   1.310720e+05                  0.000000e+00   \n",
              "75%                   3.870720e+05                  0.000000e+00   \n",
              "max                   8.304108e+08                  2.906159e+09   \n",
              "\n",
              "       ImageDirectoryEntrySecurity  \n",
              "count                 1.961100e+04  \n",
              "mean                  8.814868e+05  \n",
              "std                   2.167579e+07  \n",
              "min                   0.000000e+00  \n",
              "25%                   0.000000e+00  \n",
              "50%                   0.000000e+00  \n",
              "75%                   3.184640e+05  \n",
              "max                   2.415919e+09  \n",
              "\n",
              "[8 rows x 78 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>e_magic</th>\n",
              "      <th>e_cblp</th>\n",
              "      <th>e_cp</th>\n",
              "      <th>e_crlc</th>\n",
              "      <th>e_cparhdr</th>\n",
              "      <th>e_minalloc</th>\n",
              "      <th>e_maxalloc</th>\n",
              "      <th>e_ss</th>\n",
              "      <th>e_sp</th>\n",
              "      <th>e_csum</th>\n",
              "      <th>...</th>\n",
              "      <th>SectionMaxChar</th>\n",
              "      <th>SectionMainChar</th>\n",
              "      <th>DirectoryEntryImport</th>\n",
              "      <th>DirectoryEntryImportSize</th>\n",
              "      <th>DirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryImport</th>\n",
              "      <th>ImageDirectoryEntryResource</th>\n",
              "      <th>ImageDirectoryEntryException</th>\n",
              "      <th>ImageDirectoryEntrySecurity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19611.0</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.00000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>19611.0</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>178.615726</td>\n",
              "      <td>71.660752</td>\n",
              "      <td>49.146958</td>\n",
              "      <td>37.370710</td>\n",
              "      <td>37.032635</td>\n",
              "      <td>64178.739687</td>\n",
              "      <td>10.418490</td>\n",
              "      <td>226.46530</td>\n",
              "      <td>29.689103</td>\n",
              "      <td>...</td>\n",
              "      <td>3.163632e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.112437</td>\n",
              "      <td>101.912804</td>\n",
              "      <td>14.131865</td>\n",
              "      <td>3.368566e+05</td>\n",
              "      <td>4.047213e+05</td>\n",
              "      <td>5.555810e+05</td>\n",
              "      <td>1.238834e+06</td>\n",
              "      <td>8.814868e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>987.200729</td>\n",
              "      <td>1445.192977</td>\n",
              "      <td>1212.201919</td>\n",
              "      <td>864.515405</td>\n",
              "      <td>915.833139</td>\n",
              "      <td>9110.755873</td>\n",
              "      <td>637.116265</td>\n",
              "      <td>1249.68033</td>\n",
              "      <td>1015.303419</td>\n",
              "      <td>...</td>\n",
              "      <td>5.860332e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.525158</td>\n",
              "      <td>127.076767</td>\n",
              "      <td>154.958102</td>\n",
              "      <td>2.181191e+07</td>\n",
              "      <td>4.704601e+06</td>\n",
              "      <td>6.772167e+06</td>\n",
              "      <td>5.868961e+07</td>\n",
              "      <td>2.167579e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.960400e+04</td>\n",
              "      <td>4.096000e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.222400e+04</td>\n",
              "      <td>1.310720e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.477080e+05</td>\n",
              "      <td>3.870720e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.184640e+05</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>59448.000000</td>\n",
              "      <td>63200.000000</td>\n",
              "      <td>64613.000000</td>\n",
              "      <td>43690.000000</td>\n",
              "      <td>43690.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>61436.000000</td>\n",
              "      <td>65464.00000</td>\n",
              "      <td>63262.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.294967e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>588.000000</td>\n",
              "      <td>4016.000000</td>\n",
              "      <td>7319.000000</td>\n",
              "      <td>2.147484e+09</td>\n",
              "      <td>5.368914e+08</td>\n",
              "      <td>8.304108e+08</td>\n",
              "      <td>2.906159e+09</td>\n",
              "      <td>2.415919e+09</td>\n",
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              "<p>8 rows × 78 columns</p>\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bb5a1277-d016-404b-b5b4-cd339e04732e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "zgtn2UKUo2Ex",
        "outputId": "242e5cec-ebb1-4f83-828e-f13a6054cbde"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          Name  e_magic  e_cblp  e_cp  e_crlc  \\\n",
              "0  VirusShare_a878ba26000edaac5c98eff4432723b3    23117     144     3       0   \n",
              "1  VirusShare_ef9130570fddc174b312b2047f5f4cf0    23117     144     3       0   \n",
              "2  VirusShare_ef84cdeba22be72a69b198213dada81a    23117     144     3       0   \n",
              "3  VirusShare_6bf3608e60ebc16cbcff6ed5467d469e    23117     144     3       0   \n",
              "4  VirusShare_2cc94d952b2efb13c7d6bbe0dd59d3fb    23117     144     3       0   \n",
              "\n",
              "   e_cparhdr  e_minalloc  e_maxalloc  e_ss  e_sp  ...  SectionMaxChar  \\\n",
              "0          4           0       65535     0   184  ...      3758096608   \n",
              "1          4           0       65535     0   184  ...      3791650880   \n",
              "2          4           0       65535     0   184  ...      3221225536   \n",
              "3          4           0       65535     0   184  ...      3224371328   \n",
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              "   SectionMainChar  DirectoryEntryImport  DirectoryEntryImportSize  \\\n",
              "0                0                     7                       152   \n",
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              "3                0                     8                       155   \n",
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              "   DirectoryEntryExport  ImageDirectoryEntryExport  ImageDirectoryEntryImport  \\\n",
              "0                     0                          0                      54440   \n",
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              "3                     0                          0                     356352   \n",
              "4                     0                          0                      61440   \n",
              "\n",
              "   ImageDirectoryEntryResource  ImageDirectoryEntryException  \\\n",
              "0                        77824                         73728   \n",
              "1                       294912                             0   \n",
              "2                        40960                             0   \n",
              "3                      1003520                             0   \n",
              "4                        73728                             0   \n",
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              "0                            0  \n",
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              "3                     14109472  \n",
              "4                        90624  \n",
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              "[5 rows x 79 columns]"
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>e_magic</th>\n",
              "      <th>e_cblp</th>\n",
              "      <th>e_cp</th>\n",
              "      <th>e_crlc</th>\n",
              "      <th>e_cparhdr</th>\n",
              "      <th>e_minalloc</th>\n",
              "      <th>e_maxalloc</th>\n",
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              "      <th>SectionMainChar</th>\n",
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              "      <th>DirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryImport</th>\n",
              "      <th>ImageDirectoryEntryResource</th>\n",
              "      <th>ImageDirectoryEntryException</th>\n",
              "      <th>ImageDirectoryEntrySecurity</th>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>VirusShare_ef9130570fddc174b312b2047f5f4cf0</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
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              "      <td>65535</td>\n",
              "      <td>0</td>\n",
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              "      <td>346112</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>23117</td>\n",
              "      <td>144</td>\n",
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              "      <td>65535</td>\n",
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              "      <td>14109472</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VirusShare_2cc94d952b2efb13c7d6bbe0dd59d3fb</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
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              "      <td>...</td>\n",
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              "      <td>90624</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 79 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4ea371e-0345-43a7-89eb-4386d8ea837f')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b4ea371e-0345-43a7-89eb-4386d8ea837f 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-b4ea371e-0345-43a7-89eb-4386d8ea837f');\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",
              "\n",
              "\n",
              "<div id=\"df-c24972f6-4245-4304-87b3-ff68b0c38eb7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c24972f6-4245-4304-87b3-ff68b0c38eb7')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c24972f6-4245-4304-87b3-ff68b0c38eb7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "149lj2DGo5RV",
        "outputId": "51145138-0c12-4903-c37b-7f684f4a95a1"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19611 entries, 0 to 19610\n",
            "Data columns (total 79 columns):\n",
            " #   Column                        Non-Null Count  Dtype  \n",
            "---  ------                        --------------  -----  \n",
            " 0   Name                          19611 non-null  object \n",
            " 1   e_magic                       19611 non-null  int64  \n",
            " 2   e_cblp                        19611 non-null  int64  \n",
            " 3   e_cp                          19611 non-null  int64  \n",
            " 4   e_crlc                        19611 non-null  int64  \n",
            " 5   e_cparhdr                     19611 non-null  int64  \n",
            " 6   e_minalloc                    19611 non-null  int64  \n",
            " 7   e_maxalloc                    19611 non-null  int64  \n",
            " 8   e_ss                          19611 non-null  int64  \n",
            " 9   e_sp                          19611 non-null  int64  \n",
            " 10  e_csum                        19611 non-null  int64  \n",
            " 11  e_ip                          19611 non-null  int64  \n",
            " 12  e_cs                          19611 non-null  int64  \n",
            " 13  e_lfarlc                      19611 non-null  int64  \n",
            " 14  e_ovno                        19611 non-null  int64  \n",
            " 15  e_oemid                       19611 non-null  int64  \n",
            " 16  e_oeminfo                     19611 non-null  int64  \n",
            " 17  e_lfanew                      19611 non-null  int64  \n",
            " 18  Machine                       19611 non-null  int64  \n",
            " 19  NumberOfSections              19611 non-null  int64  \n",
            " 20  TimeDateStamp                 19611 non-null  int64  \n",
            " 21  PointerToSymbolTable          19611 non-null  int64  \n",
            " 22  NumberOfSymbols               19611 non-null  int64  \n",
            " 23  SizeOfOptionalHeader          19611 non-null  int64  \n",
            " 24  Characteristics               19611 non-null  int64  \n",
            " 25  Magic                         19611 non-null  int64  \n",
            " 26  MajorLinkerVersion            19611 non-null  int64  \n",
            " 27  MinorLinkerVersion            19611 non-null  int64  \n",
            " 28  SizeOfCode                    19611 non-null  int64  \n",
            " 29  SizeOfInitializedData         19611 non-null  int64  \n",
            " 30  SizeOfUninitializedData       19611 non-null  int64  \n",
            " 31  AddressOfEntryPoint           19611 non-null  int64  \n",
            " 32  BaseOfCode                    19611 non-null  int64  \n",
            " 33  ImageBase                     19611 non-null  int64  \n",
            " 34  SectionAlignment              19611 non-null  int64  \n",
            " 35  FileAlignment                 19611 non-null  int64  \n",
            " 36  MajorOperatingSystemVersion   19611 non-null  int64  \n",
            " 37  MinorOperatingSystemVersion   19611 non-null  int64  \n",
            " 38  MajorImageVersion             19611 non-null  int64  \n",
            " 39  MinorImageVersion             19611 non-null  int64  \n",
            " 40  MajorSubsystemVersion         19611 non-null  int64  \n",
            " 41  MinorSubsystemVersion         19611 non-null  int64  \n",
            " 42  SizeOfHeaders                 19611 non-null  int64  \n",
            " 43  CheckSum                      19611 non-null  int64  \n",
            " 44  SizeOfImage                   19611 non-null  int64  \n",
            " 45  Subsystem                     19611 non-null  int64  \n",
            " 46  DllCharacteristics            19611 non-null  int64  \n",
            " 47  SizeOfStackReserve            19611 non-null  int64  \n",
            " 48  SizeOfStackCommit             19611 non-null  int64  \n",
            " 49  SizeOfHeapReserve             19611 non-null  int64  \n",
            " 50  SizeOfHeapCommit              19611 non-null  int64  \n",
            " 51  LoaderFlags                   19611 non-null  int64  \n",
            " 52  NumberOfRvaAndSizes           19611 non-null  int64  \n",
            " 53  Malware                       19611 non-null  int64  \n",
            " 54  SuspiciousImportFunctions     19611 non-null  int64  \n",
            " 55  SuspiciousNameSection         19611 non-null  int64  \n",
            " 56  SectionsLength                19611 non-null  int64  \n",
            " 57  SectionMinEntropy             19611 non-null  float64\n",
            " 58  SectionMaxEntropy             19611 non-null  int64  \n",
            " 59  SectionMinRawsize             19611 non-null  int64  \n",
            " 60  SectionMaxRawsize             19611 non-null  int64  \n",
            " 61  SectionMinVirtualsize         19611 non-null  int64  \n",
            " 62  SectionMaxVirtualsize         19611 non-null  int64  \n",
            " 63  SectionMaxPhysical            19611 non-null  int64  \n",
            " 64  SectionMinPhysical            19611 non-null  int64  \n",
            " 65  SectionMaxVirtual             19611 non-null  int64  \n",
            " 66  SectionMinVirtual             19611 non-null  int64  \n",
            " 67  SectionMaxPointerData         19611 non-null  int64  \n",
            " 68  SectionMinPointerData         19611 non-null  int64  \n",
            " 69  SectionMaxChar                19611 non-null  int64  \n",
            " 70  SectionMainChar               19611 non-null  int64  \n",
            " 71  DirectoryEntryImport          19611 non-null  int64  \n",
            " 72  DirectoryEntryImportSize      19611 non-null  int64  \n",
            " 73  DirectoryEntryExport          19611 non-null  int64  \n",
            " 74  ImageDirectoryEntryExport     19611 non-null  int64  \n",
            " 75  ImageDirectoryEntryImport     19611 non-null  int64  \n",
            " 76  ImageDirectoryEntryResource   19611 non-null  int64  \n",
            " 77  ImageDirectoryEntryException  19611 non-null  int64  \n",
            " 78  ImageDirectoryEntrySecurity   19611 non-null  int64  \n",
            "dtypes: float64(1), int64(77), object(1)\n",
            "memory usage: 11.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(['Name', 'Machine', 'TimeDateStamp', 'SectionMainChar'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "XDFZimZgo8iN"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOdyTay9pW0t",
        "outputId": "ac1d0899-ee3e-45da-de90-5200827aa6b1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19611, 75)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Malware'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "SMUWVdhlpiSP",
        "outputId": "6c9c8894-2758-4ba4-ac4a-297ca46ad074"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "features = data.drop('Malware', axis=1)\n",
        "\n",
        "targets = data['Malware']\n",
        "\n",
        "features_scaled = scaler.fit_transform(features)\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(features_scaled, targets, test_size=0.2, stratify=targets)\n",
        "\n"
      ],
      "metadata": {
        "id": "ywke__t4p3E-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwiHB_V0toAY",
        "outputId": "89629490-437e-40de-dcd3-1388c8e7d920"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        5.03114873e-04, 0.00000000e+00, 0.00000000e+00],\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        2.95949925e-05, 0.00000000e+00, 0.00000000e+00],\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        3.45274913e-05, 0.00000000e+00, 0.00000000e+00],\n",
              "       ...,\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        9.86499751e-06, 0.00000000e+00, 0.00000000e+00],\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        4.43924888e-05, 0.00000000e+00, 0.00000000e+00],\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        8.87849776e-05, 0.00000000e+00, 2.64909532e-05]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNEsJfvsttqG",
        "outputId": "1dc385e9-c72f-4e49-f80a-9efd83a5e229"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        1.43042464e-04, 3.80543548e-05, 0.00000000e+00],\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        9.66769756e-04, 0.00000000e+00, 0.00000000e+00],\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        4.43924888e-05, 0.00000000e+00, 0.00000000e+00],\n",
              "       ...,\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        1.23805719e-03, 0.00000000e+00, 5.74431154e-03],\n",
              "       [0.00000000e+00, 1.34571390e-03, 3.16455696e-05, ...,\n",
              "        8.38524788e-05, 0.00000000e+00, 4.02755207e-04],\n",
              "       [0.00000000e+00, 2.42228502e-03, 4.74683544e-05, ...,\n",
              "        7.89199801e-05, 0.00000000e+00, 0.00000000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training ML Algorithms"
      ],
      "metadata": {
        "id": "oUafaJnpyugw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n"
      ],
      "metadata": {
        "id": "XO5neQggtxg7"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier()\n",
        "\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "_iGWS4kLzSlS",
        "outputId": "b63d0530-14b2-4101-dae2-959864a63fce"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, knn.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97qkxe-4zb_S",
        "outputId": "c6194149-3909-41d7-97d7-af2f5fb3ffa2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.975274024980882"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestClassifier()\n",
        "\n",
        "rfc.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "ubWSRysbzoXE",
        "outputId": "2bb8344e-ab67-4e03-c614-147983dedc53"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, rfc.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SvOQpdLz1_q",
        "outputId": "4d96520f-8fc4-436c-b31b-328fdef2a98a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9887840938057609"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "yIJzZQnG0Alz",
        "outputId": "b66f52f7-aeb3-447f-d93a-e9aebd1e1de8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, lr.predict(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oh9AFEtd0MnG",
        "outputId": "cf237322-898d-47ec-fec8-d273bbd360fb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9602345144022432"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save the Best Model"
      ],
      "metadata": {
        "id": "2iR2g94p0skS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "DyS1dVRI0oAL"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_file = \"malware_predictor.pkl\""
      ],
      "metadata": {
        "id": "n03Lkoop0VY_"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(model_file, 'wb') as file :\n",
        "  pickle.dump(rfc, file)"
      ],
      "metadata": {
        "id": "_hztoAVj0zfJ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test saved model"
      ],
      "metadata": {
        "id": "iXKjgXnn1G0V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(model_file, 'rb') as file :\n",
        "  rfc_loaded = pickle.load(file)"
      ],
      "metadata": {
        "id": "i3wy-_kY079G"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_loaded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "42iAfOda1Www",
        "outputId": "89629451-e040-46d4-e726-bb284433d0df"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_loaded.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1CSUqgs1Y6L",
        "outputId": "63896620-297a-4d17-a37c-e1341a9dabfe"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
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      "source": [],
      "metadata": {
        "id": "5wdzP-RZ1dI-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}