MiniProject.ipynb

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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2hUKKpt8dwCh"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ZlBOCpxZjzEF"
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      "outputs": [],
      "source": [
        "DataPath=\"data/MalwareData.csv\"\n"
      ]
    },
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              "           Name                               md5  Machine  \\\n",
              "0   memtest.exe  631ea355665f28d4707448e442fbf5b8      332   \n",
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              "4  dwtrig20.exe  c87e561258f2f8650cef999bf643a731      332   \n",
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              "   SizeOfOptionalHeader  Characteristics  MajorLinkerVersion  \\\n",
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              "   MinorLinkerVersion  SizeOfCode  SizeOfInitializedData  \\\n",
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              "3                   0      585728                 369152   \n",
              "4                   0      294912                 247296   \n",
              "\n",
              "   SizeOfUninitializedData  ...  ResourcesNb  ResourcesMeanEntropy  \\\n",
              "0                        0  ...            4              3.262823   \n",
              "1                        0  ...            2              4.250461   \n",
              "2                        0  ...           11              4.426324   \n",
              "3                        0  ...           10              4.364291   \n",
              "4                        0  ...            2              4.306100   \n",
              "\n",
              "   ResourcesMinEntropy  ResourcesMaxEntropy  ResourcesMeanSize  \\\n",
              "0             2.568844             3.537939        8797.000000   \n",
              "1             3.420744             5.080177         837.000000   \n",
              "2             2.846449             5.271813       31102.272727   \n",
              "3             2.669314             6.400720        1457.000000   \n",
              "4             3.421598             5.190603        1074.500000   \n",
              "\n",
              "   ResourcesMinSize  ResourcesMaxSize  LoadConfigurationSize  \\\n",
              "0               216             18032                      0   \n",
              "1               518              1156                     72   \n",
              "2               104            270376                     72   \n",
              "3                90              4264                     72   \n",
              "4               849              1300                     72   \n",
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              "   VersionInformationSize  legitimate  \n",
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              "      <td>a41e524f8d45f0074fd07805ff0c9b12</td>\n",
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              "            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",
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data = pd.read_csv(DataPath,sep=\"|\")\n",
        "data.head()"
      ]
    },
    {
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        "outputId": "fcad6286-d6fd-429e-a6a2-c134db856ff0"
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              "               Name                               md5  Machine  \\\n",
              "0       memtest.exe  631ea355665f28d4707448e442fbf5b8      332   \n",
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              "4      dwtrig20.exe  c87e561258f2f8650cef999bf643a731      332   \n",
              "...             ...                               ...      ...   \n",
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              "\n",
              "       SizeOfOptionalHeader  Characteristics  MajorLinkerVersion  \\\n",
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              "41321                   224             8450                   8   \n",
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              "\n",
              "       MinorLinkerVersion  SizeOfCode  SizeOfInitializedData  \\\n",
              "0                       0      361984                 115712   \n",
              "1                       0      130560                  19968   \n",
              "2                       0      517120                 621568   \n",
              "3                       0      585728                 369152   \n",
              "4                       0      294912                 247296   \n",
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              "41318                   0      946176                 159744   \n",
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              "41320                   0       53248                  16384   \n",
              "41321                   0       52736                  11264   \n",
              "41322                   0       40960                  20480   \n",
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              "       SizeOfUninitializedData  ...  ExportNb  ResourcesNb  \\\n",
              "0                            0  ...         0            4   \n",
              "1                            0  ...         0            2   \n",
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              "41321                        0  ...        25            1   \n",
              "41322                        0  ...       112            6   \n",
              "\n",
              "       ResourcesMeanEntropy  ResourcesMinEntropy  ResourcesMaxEntropy  \\\n",
              "0                  3.262823             2.568844             3.537939   \n",
              "1                  4.250461             3.420744             5.080177   \n",
              "2                  4.426324             2.846449             5.271813   \n",
              "3                  4.364291             2.669314             6.400720   \n",
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              "41321              3.542071             3.542071             3.542071   \n",
              "41322              3.004383             2.406512             3.592623   \n",
              "\n",
              "       ResourcesMeanSize  ResourcesMinSize  ResourcesMaxSize  \\\n",
              "0            8797.000000               216             18032   \n",
              "1             837.000000               518              1156   \n",
              "2           31102.272727               104            270376   \n",
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              "       LoadConfigurationSize  VersionInformationSize  \n",
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              "      <td>631ea355665f28d4707448e442fbf5b8</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>361984</td>\n",
              "      <td>115712</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3.262823</td>\n",
              "      <td>2.568844</td>\n",
              "      <td>3.537939</td>\n",
              "      <td>8797.000000</td>\n",
              "      <td>216</td>\n",
              "      <td>18032</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ose.exe</td>\n",
              "      <td>9d10f99a6712e28f8acd5641e3a7ea6b</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>3330</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>130560</td>\n",
              "      <td>19968</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4.250461</td>\n",
              "      <td>3.420744</td>\n",
              "      <td>5.080177</td>\n",
              "      <td>837.000000</td>\n",
              "      <td>518</td>\n",
              "      <td>1156</td>\n",
              "      <td>72</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>setup.exe</td>\n",
              "      <td>4d92f518527353c0db88a70fddcfd390</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>3330</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>517120</td>\n",
              "      <td>621568</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>4.426324</td>\n",
              "      <td>2.846449</td>\n",
              "      <td>5.271813</td>\n",
              "      <td>31102.272727</td>\n",
              "      <td>104</td>\n",
              "      <td>270376</td>\n",
              "      <td>72</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DW20.EXE</td>\n",
              "      <td>a41e524f8d45f0074fd07805ff0c9b12</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>585728</td>\n",
              "      <td>369152</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>4.364291</td>\n",
              "      <td>2.669314</td>\n",
              "      <td>6.400720</td>\n",
              "      <td>1457.000000</td>\n",
              "      <td>90</td>\n",
              "      <td>4264</td>\n",
              "      <td>72</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>dwtrig20.exe</td>\n",
              "      <td>c87e561258f2f8650cef999bf643a731</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>294912</td>\n",
              "      <td>247296</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4.306100</td>\n",
              "      <td>3.421598</td>\n",
              "      <td>5.190603</td>\n",
              "      <td>1074.500000</td>\n",
              "      <td>849</td>\n",
              "      <td>1300</td>\n",
              "      <td>72</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41318</th>\n",
              "      <td>mfc80.dll</td>\n",
              "      <td>1f5afd468eb5e09e9ed75a087529eab5</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>8450</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>946176</td>\n",
              "      <td>159744</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "      <td>2.607251</td>\n",
              "      <td>0.960953</td>\n",
              "      <td>5.130762</td>\n",
              "      <td>327.170732</td>\n",
              "      <td>20</td>\n",
              "      <td>1592</td>\n",
              "      <td>72</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41319</th>\n",
              "      <td>mfc80u.dll</td>\n",
              "      <td>e2c48cd0132d4d1dc7d0df9a6bef686a</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>8450</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>946176</td>\n",
              "      <td>154624</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>123</td>\n",
              "      <td>2.607232</td>\n",
              "      <td>0.960953</td>\n",
              "      <td>5.130762</td>\n",
              "      <td>327.235772</td>\n",
              "      <td>20</td>\n",
              "      <td>1592</td>\n",
              "      <td>72</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41320</th>\n",
              "      <td>mfcm80.dll</td>\n",
              "      <td>83362ee950ad18adb85b54409155c378</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>8450</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>53248</td>\n",
              "      <td>16384</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>3.524268</td>\n",
              "      <td>3.524268</td>\n",
              "      <td>3.524268</td>\n",
              "      <td>892.000000</td>\n",
              "      <td>892</td>\n",
              "      <td>892</td>\n",
              "      <td>72</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41321</th>\n",
              "      <td>mfcm80u.dll</td>\n",
              "      <td>26aafee5c30020c99120ee113d751f7e</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>8450</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>52736</td>\n",
              "      <td>11264</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>3.542071</td>\n",
              "      <td>3.542071</td>\n",
              "      <td>3.542071</td>\n",
              "      <td>892.000000</td>\n",
              "      <td>892</td>\n",
              "      <td>892</td>\n",
              "      <td>72</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41322</th>\n",
              "      <td>vcomp.dll</td>\n",
              "      <td>73dbaa64d589f3262615550dd6881fee</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>8450</td>\n",
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              "      <td>6</td>\n",
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              "      <td>2.406512</td>\n",
              "      <td>3.592623</td>\n",
              "      <td>610.333333</td>\n",
              "      <td>124</td>\n",
              "      <td>1412</td>\n",
              "      <td>72</td>\n",
              "      <td>16</td>\n",
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              "  </tbody>\n",
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              "<p>41323 rows × 56 columns</p>\n",
              "</div>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "legit = data[0:41323].drop(['legitimate'],axis=1)\n",
        "legit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Z1v91XLCuj2N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "a1454e6b-cb06-4a7a-f5e6-d4c9c33a1a15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Name  \\\n",
              "41323   VirusShare_4a400b747afe6547e09ce0b02dae7f1c   \n",
              "41324   VirusShare_9bd57c8252948bd2fa651ad372bd4f13   \n",
              "41325   VirusShare_d1456165e9358b8f61f93a5f2042f39c   \n",
              "41326   VirusShare_e4214cc73afbba0f52bb72d5db8f8bb1   \n",
              "41327   VirusShare_710890c07b3f93b90635f8bff6c34605   \n",
              "...                                             ...   \n",
              "138042  VirusShare_8e292b418568d6e7b87f2a32aee7074b   \n",
              "138043  VirusShare_260d9e2258aed4c8a3bbd703ec895822   \n",
              "138044  VirusShare_8d088a51b7d225c9f5d11d239791ec3f   \n",
              "138045  VirusShare_4286dccf67ca220fe67635388229a9f3   \n",
              "138046  VirusShare_d7648eae45f09b3adb75127f43be6d11   \n",
              "\n",
              "                                     md5  Machine  SizeOfOptionalHeader  \\\n",
              "41323   4a400b747afe6547e09ce0b02dae7f1c      332                   224   \n",
              "41324   9bd57c8252948bd2fa651ad372bd4f13      332                   224   \n",
              "41325   d1456165e9358b8f61f93a5f2042f39c      332                   224   \n",
              "41326   e4214cc73afbba0f52bb72d5db8f8bb1      332                   224   \n",
              "41327   710890c07b3f93b90635f8bff6c34605      332                   224   \n",
              "...                                  ...      ...                   ...   \n",
              "138042  8e292b418568d6e7b87f2a32aee7074b      332                   224   \n",
              "138043  260d9e2258aed4c8a3bbd703ec895822      332                   224   \n",
              "138044  8d088a51b7d225c9f5d11d239791ec3f      332                   224   \n",
              "138045  4286dccf67ca220fe67635388229a9f3      332                   224   \n",
              "138046  d7648eae45f09b3adb75127f43be6d11      332                   224   \n",
              "\n",
              "        Characteristics  MajorLinkerVersion  MinorLinkerVersion  SizeOfCode  \\\n",
              "41323               258                  11                   0      354816   \n",
              "41324               271                   6                   0       24064   \n",
              "41325               258                  10                   0      118784   \n",
              "41326               258                  10                   0      174592   \n",
              "41327               258                   9                   0      475648   \n",
              "...                 ...                 ...                 ...         ...   \n",
              "138042              258                  11                   0      205824   \n",
              "138043            33167                   2                  25       37888   \n",
              "138044              258                  10                   0      118272   \n",
              "138045            33166                   2                  25       49152   \n",
              "138046              258                  11                   0      111616   \n",
              "\n",
              "        SizeOfInitializedData  SizeOfUninitializedData  ...  ExportNb  \\\n",
              "41323                  257024                        0  ...         0   \n",
              "41324                  164864                     1024  ...         0   \n",
              "41325                  381952                        0  ...         0   \n",
              "41326                  300032                        0  ...         0   \n",
              "41327                  348672                        0  ...         0   \n",
              "...                       ...                      ...  ...       ...   \n",
              "138042                 223744                        0  ...         0   \n",
              "138043                 185344                        0  ...         0   \n",
              "138044                 380416                        0  ...         0   \n",
              "138045                  16896                        0  ...         0   \n",
              "138046                 468480                        0  ...         0   \n",
              "\n",
              "        ResourcesNb  ResourcesMeanEntropy  ResourcesMinEntropy  \\\n",
              "41323             7              3.914415             1.441688   \n",
              "41324             6              3.199107             1.971335   \n",
              "41325            18              6.530946             2.458492   \n",
              "41326            15              5.732393             2.852364   \n",
              "41327            59              2.827826             0.960953   \n",
              "...             ...                   ...                  ...   \n",
              "138042            7              4.122736             1.370260   \n",
              "138043           26              3.377663             2.031619   \n",
              "138044           22              6.825406             2.617026   \n",
              "138045           10              3.421627             2.060964   \n",
              "138046            4              4.407252             1.980482   \n",
              "\n",
              "        ResourcesMaxEntropy  ResourcesMeanSize  ResourcesMinSize  \\\n",
              "41323              7.677091        7298.428571                16   \n",
              "41324              5.214816         452.000000                34   \n",
              "41325              7.992688       18523.444444                48   \n",
              "41326              7.987726       12706.133333               118   \n",
              "41327              7.212329        2637.033898                20   \n",
              "...                     ...                ...               ...   \n",
              "138042             7.677091       14900.714286                16   \n",
              "138043             5.050074        6905.846154                44   \n",
              "138044             7.990487       14981.909091                48   \n",
              "138045             4.739744         601.600000                16   \n",
              "138046             6.115374       96625.000000                20   \n",
              "\n",
              "        ResourcesMaxSize  LoadConfigurationSize  VersionInformationSize  \n",
              "41323              28438                     72                       0  \n",
              "41324                958                      0                      15  \n",
              "41325              33945                     72                      14  \n",
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              "41327              67624                     72                       0  \n",
              "...                  ...                    ...                     ...  \n",
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              "138046            318464                     72                       0  \n",
              "\n",
              "[96724 rows x 56 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>md5</th>\n",
              "      <th>Machine</th>\n",
              "      <th>SizeOfOptionalHeader</th>\n",
              "      <th>Characteristics</th>\n",
              "      <th>MajorLinkerVersion</th>\n",
              "      <th>MinorLinkerVersion</th>\n",
              "      <th>SizeOfCode</th>\n",
              "      <th>SizeOfInitializedData</th>\n",
              "      <th>SizeOfUninitializedData</th>\n",
              "      <th>...</th>\n",
              "      <th>ExportNb</th>\n",
              "      <th>ResourcesNb</th>\n",
              "      <th>ResourcesMeanEntropy</th>\n",
              "      <th>ResourcesMinEntropy</th>\n",
              "      <th>ResourcesMaxEntropy</th>\n",
              "      <th>ResourcesMeanSize</th>\n",
              "      <th>ResourcesMinSize</th>\n",
              "      <th>ResourcesMaxSize</th>\n",
              "      <th>LoadConfigurationSize</th>\n",
              "      <th>VersionInformationSize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41323</th>\n",
              "      <td>VirusShare_4a400b747afe6547e09ce0b02dae7f1c</td>\n",
              "      <td>4a400b747afe6547e09ce0b02dae7f1c</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>354816</td>\n",
              "      <td>257024</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3.914415</td>\n",
              "      <td>1.441688</td>\n",
              "      <td>7.677091</td>\n",
              "      <td>7298.428571</td>\n",
              "      <td>16</td>\n",
              "      <td>28438</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41324</th>\n",
              "      <td>VirusShare_9bd57c8252948bd2fa651ad372bd4f13</td>\n",
              "      <td>9bd57c8252948bd2fa651ad372bd4f13</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>271</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>24064</td>\n",
              "      <td>164864</td>\n",
              "      <td>1024</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>3.199107</td>\n",
              "      <td>1.971335</td>\n",
              "      <td>5.214816</td>\n",
              "      <td>452.000000</td>\n",
              "      <td>34</td>\n",
              "      <td>958</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41325</th>\n",
              "      <td>VirusShare_d1456165e9358b8f61f93a5f2042f39c</td>\n",
              "      <td>d1456165e9358b8f61f93a5f2042f39c</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>118784</td>\n",
              "      <td>381952</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>6.530946</td>\n",
              "      <td>2.458492</td>\n",
              "      <td>7.992688</td>\n",
              "      <td>18523.444444</td>\n",
              "      <td>48</td>\n",
              "      <td>33945</td>\n",
              "      <td>72</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41326</th>\n",
              "      <td>VirusShare_e4214cc73afbba0f52bb72d5db8f8bb1</td>\n",
              "      <td>e4214cc73afbba0f52bb72d5db8f8bb1</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>174592</td>\n",
              "      <td>300032</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>5.732393</td>\n",
              "      <td>2.852364</td>\n",
              "      <td>7.987726</td>\n",
              "      <td>12706.133333</td>\n",
              "      <td>118</td>\n",
              "      <td>60500</td>\n",
              "      <td>72</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41327</th>\n",
              "      <td>VirusShare_710890c07b3f93b90635f8bff6c34605</td>\n",
              "      <td>710890c07b3f93b90635f8bff6c34605</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>475648</td>\n",
              "      <td>348672</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>59</td>\n",
              "      <td>2.827826</td>\n",
              "      <td>0.960953</td>\n",
              "      <td>7.212329</td>\n",
              "      <td>2637.033898</td>\n",
              "      <td>20</td>\n",
              "      <td>67624</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138042</th>\n",
              "      <td>VirusShare_8e292b418568d6e7b87f2a32aee7074b</td>\n",
              "      <td>8e292b418568d6e7b87f2a32aee7074b</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>205824</td>\n",
              "      <td>223744</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4.122736</td>\n",
              "      <td>1.370260</td>\n",
              "      <td>7.677091</td>\n",
              "      <td>14900.714286</td>\n",
              "      <td>16</td>\n",
              "      <td>81654</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138043</th>\n",
              "      <td>VirusShare_260d9e2258aed4c8a3bbd703ec895822</td>\n",
              "      <td>260d9e2258aed4c8a3bbd703ec895822</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>33167</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>37888</td>\n",
              "      <td>185344</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>3.377663</td>\n",
              "      <td>2.031619</td>\n",
              "      <td>5.050074</td>\n",
              "      <td>6905.846154</td>\n",
              "      <td>44</td>\n",
              "      <td>67624</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138044</th>\n",
              "      <td>VirusShare_8d088a51b7d225c9f5d11d239791ec3f</td>\n",
              "      <td>8d088a51b7d225c9f5d11d239791ec3f</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>118272</td>\n",
              "      <td>380416</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>6.825406</td>\n",
              "      <td>2.617026</td>\n",
              "      <td>7.990487</td>\n",
              "      <td>14981.909091</td>\n",
              "      <td>48</td>\n",
              "      <td>22648</td>\n",
              "      <td>72</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138045</th>\n",
              "      <td>VirusShare_4286dccf67ca220fe67635388229a9f3</td>\n",
              "      <td>4286dccf67ca220fe67635388229a9f3</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>33166</td>\n",
              "      <td>2</td>\n",
              "      <td>25</td>\n",
              "      <td>49152</td>\n",
              "      <td>16896</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>3.421627</td>\n",
              "      <td>2.060964</td>\n",
              "      <td>4.739744</td>\n",
              "      <td>601.600000</td>\n",
              "      <td>16</td>\n",
              "      <td>2216</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138046</th>\n",
              "      <td>VirusShare_d7648eae45f09b3adb75127f43be6d11</td>\n",
              "      <td>d7648eae45f09b3adb75127f43be6d11</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
              "      <td>258</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>111616</td>\n",
              "      <td>468480</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.407252</td>\n",
              "      <td>1.980482</td>\n",
              "      <td>6.115374</td>\n",
              "      <td>96625.000000</td>\n",
              "      <td>20</td>\n",
              "      <td>318464</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96724 rows × 56 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f42d5424-8856-4b1d-9662-d4e6d91ce673')\"\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",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f42d5424-8856-4b1d-9662-d4e6d91ce673');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "malware = data[41323::].drop(['legitimate'],axis=1)\n",
        "malware"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "nTngqCRcvICn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed7f113f-3fa0-4a2d-f963-41ce6effdf8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Name', 'md5', 'Machine', 'SizeOfOptionalHeader', 'Characteristics',\n",
              "       'MajorLinkerVersion', 'MinorLinkerVersion', 'SizeOfCode',\n",
              "       'SizeOfInitializedData', 'SizeOfUninitializedData',\n",
              "       'AddressOfEntryPoint', 'BaseOfCode', 'BaseOfData', 'ImageBase',\n",
              "       'SectionAlignment', 'FileAlignment', 'MajorOperatingSystemVersion',\n",
              "       'MinorOperatingSystemVersion', 'MajorImageVersion', 'MinorImageVersion',\n",
              "       'MajorSubsystemVersion', 'MinorSubsystemVersion', 'SizeOfImage',\n",
              "       'SizeOfHeaders', 'CheckSum', 'Subsystem', 'DllCharacteristics',\n",
              "       'SizeOfStackReserve', 'SizeOfStackCommit', 'SizeOfHeapReserve',\n",
              "       'SizeOfHeapCommit', 'LoaderFlags', 'NumberOfRvaAndSizes', 'SectionsNb',\n",
              "       'SectionsMeanEntropy', 'SectionsMinEntropy', 'SectionsMaxEntropy',\n",
              "       'SectionsMeanRawsize', 'SectionsMinRawsize', 'SectionMaxRawsize',\n",
              "       'SectionsMeanVirtualsize', 'SectionsMinVirtualsize',\n",
              "       'SectionMaxVirtualsize', 'ImportsNbDLL', 'ImportsNb',\n",
              "       'ImportsNbOrdinal', 'ExportNb', 'ResourcesNb', 'ResourcesMeanEntropy',\n",
              "       'ResourcesMinEntropy', 'ResourcesMaxEntropy', 'ResourcesMeanSize',\n",
              "       'ResourcesMinSize', 'ResourcesMaxSize', 'LoadConfigurationSize',\n",
              "       'VersionInformationSize', 'legitimate'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "LcpRptrAvN92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "17060b05-3c85-45ef-8561-24ffd9e39b2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Name  \\\n",
              "0                                       memtest.exe   \n",
              "1                                           ose.exe   \n",
              "2                                         setup.exe   \n",
              "3                                          DW20.EXE   \n",
              "4                                      dwtrig20.exe   \n",
              "...                                             ...   \n",
              "138042  VirusShare_8e292b418568d6e7b87f2a32aee7074b   \n",
              "138043  VirusShare_260d9e2258aed4c8a3bbd703ec895822   \n",
              "138044  VirusShare_8d088a51b7d225c9f5d11d239791ec3f   \n",
              "138045  VirusShare_4286dccf67ca220fe67635388229a9f3   \n",
              "138046  VirusShare_d7648eae45f09b3adb75127f43be6d11   \n",
              "\n",
              "                                     md5  Machine  SizeOfOptionalHeader  \\\n",
              "0       631ea355665f28d4707448e442fbf5b8      332                   224   \n",
              "1       9d10f99a6712e28f8acd5641e3a7ea6b      332                   224   \n",
              "2       4d92f518527353c0db88a70fddcfd390      332                   224   \n",
              "3       a41e524f8d45f0074fd07805ff0c9b12      332                   224   \n",
              "4       c87e561258f2f8650cef999bf643a731      332                   224   \n",
              "...                                  ...      ...                   ...   \n",
              "138042  8e292b418568d6e7b87f2a32aee7074b      332                   224   \n",
              "138043  260d9e2258aed4c8a3bbd703ec895822      332                   224   \n",
              "138044  8d088a51b7d225c9f5d11d239791ec3f      332                   224   \n",
              "138045  4286dccf67ca220fe67635388229a9f3      332                   224   \n",
              "138046  d7648eae45f09b3adb75127f43be6d11      332                   224   \n",
              "\n",
              "        Characteristics  MajorLinkerVersion  MinorLinkerVersion  SizeOfCode  \\\n",
              "0                   258                   9                   0      361984   \n",
              "1                  3330                   9                   0      130560   \n",
              "2                  3330                   9                   0      517120   \n",
              "3                   258                   9                   0      585728   \n",
              "4                   258                   9                   0      294912   \n",
              "...                 ...                 ...                 ...         ...   \n",
              "138042              258                  11                   0      205824   \n",
              "138043            33167                   2                  25       37888   \n",
              "138044              258                  10                   0      118272   \n",
              "138045            33166                   2                  25       49152   \n",
              "138046              258                  11                   0      111616   \n",
              "\n",
              "        SizeOfInitializedData  SizeOfUninitializedData  ...  ResourcesNb  \\\n",
              "0                      115712                        0  ...            4   \n",
              "1                       19968                        0  ...            2   \n",
              "2                      621568                        0  ...           11   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "E7jkbnUbvlvT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6b4e920-fe5c-402c-9b58-f823c791d6e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(138047, 57)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ZiL1Fp7xvp4A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1eb1a2b8-8c93-446a-d43a-6d45c6ed4c85"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1\n",
              "1         1\n",
              "2         1\n",
              "3         1\n",
              "4         1\n",
              "         ..\n",
              "138042    0\n",
              "138043    0\n",
              "138044    0\n",
              "138045    0\n",
              "138046    0\n",
              "Name: legitimate, Length: 138047, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "target = data['legitimate']\n",
        "target "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "OxFeVyHFlPAg"
      },
      "outputs": [],
      "source": [
        "working_data = data.drop(['legitimate'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "15qAO2wYlloi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "b8dc51f5-b3a1-4029-d4b6-fca0264cd01c"
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      "outputs": [
        {
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          "data": {
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              "                                               Name  \\\n",
              "0                                       memtest.exe   \n",
              "1                                           ose.exe   \n",
              "2                                         setup.exe   \n",
              "3                                          DW20.EXE   \n",
              "4                                      dwtrig20.exe   \n",
              "...                                             ...   \n",
              "138042  VirusShare_8e292b418568d6e7b87f2a32aee7074b   \n",
              "138043  VirusShare_260d9e2258aed4c8a3bbd703ec895822   \n",
              "138044  VirusShare_8d088a51b7d225c9f5d11d239791ec3f   \n",
              "138045  VirusShare_4286dccf67ca220fe67635388229a9f3   \n",
              "138046  VirusShare_d7648eae45f09b3adb75127f43be6d11   \n",
              "\n",
              "                                     md5  Machine  SizeOfOptionalHeader  \\\n",
              "0       631ea355665f28d4707448e442fbf5b8      332                   224   \n",
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              "\n",
              "        Characteristics  MajorLinkerVersion  MinorLinkerVersion  SizeOfCode  \\\n",
              "0                   258                   9                   0      361984   \n",
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              "\n",
              "        SizeOfInitializedData  SizeOfUninitializedData  ...  ExportNb  \\\n",
              "0                      115712                        0  ...         0   \n",
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              "138045                  16896                        0  ...         0   \n",
              "138046                 468480                        0  ...         0   \n",
              "\n",
              "        ResourcesNb  ResourcesMeanEntropy  ResourcesMinEntropy  \\\n",
              "0                 4              3.262823             2.568844   \n",
              "1                 2              4.250461             3.420744   \n",
              "2                11              4.426324             2.846449   \n",
              "3                10              4.364291             2.669314   \n",
              "4                 2              4.306100             3.421598   \n",
              "...             ...                   ...                  ...   \n",
              "138042            7              4.122736             1.370260   \n",
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              "138045           10              3.421627             2.060964   \n",
              "138046            4              4.407252             1.980482   \n",
              "\n",
              "        ResourcesMaxEntropy  ResourcesMeanSize  ResourcesMinSize  \\\n",
              "0                  3.537939        8797.000000               216   \n",
              "1                  5.080177         837.000000               518   \n",
              "2                  5.271813       31102.272727               104   \n",
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              "...                     ...                ...               ...   \n",
              "138042             7.677091       14900.714286                16   \n",
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              "138045             4.739744         601.600000                16   \n",
              "138046             6.115374       96625.000000                20   \n",
              "\n",
              "        ResourcesMaxSize  LoadConfigurationSize  VersionInformationSize  \n",
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              "3                   4264                     72                      18  \n",
              "4                   1300                     72                      18  \n",
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              "138042             81654                     72                       0  \n",
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              "138044             22648                     72                      14  \n",
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              "      <th>2</th>\n",
              "      <td>setup.exe</td>\n",
              "      <td>4d92f518527353c0db88a70fddcfd390</td>\n",
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              "      <td>11</td>\n",
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              "      <td>5.271813</td>\n",
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              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DW20.EXE</td>\n",
              "      <td>a41e524f8d45f0074fd07805ff0c9b12</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
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              "      <td>585728</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>dwtrig20.exe</td>\n",
              "      <td>c87e561258f2f8650cef999bf643a731</td>\n",
              "      <td>332</td>\n",
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              "    <tr>\n",
              "      <th>138042</th>\n",
              "      <td>VirusShare_8e292b418568d6e7b87f2a32aee7074b</td>\n",
              "      <td>8e292b418568d6e7b87f2a32aee7074b</td>\n",
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              "      <td>7.677091</td>\n",
              "      <td>14900.714286</td>\n",
              "      <td>16</td>\n",
              "      <td>81654</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138043</th>\n",
              "      <td>VirusShare_260d9e2258aed4c8a3bbd703ec895822</td>\n",
              "      <td>260d9e2258aed4c8a3bbd703ec895822</td>\n",
              "      <td>332</td>\n",
              "      <td>224</td>\n",
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              "    <tr>\n",
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              "      <td>VirusShare_8d088a51b7d225c9f5d11d239791ec3f</td>\n",
              "      <td>8d088a51b7d225c9f5d11d239791ec3f</td>\n",
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              "    <tr>\n",
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              "      <td>VirusShare_4286dccf67ca220fe67635388229a9f3</td>\n",
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              "      <td>VirusShare_d7648eae45f09b3adb75127f43be6d11</td>\n",
              "      <td>d7648eae45f09b3adb75127f43be6d11</td>\n",
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              "                                                     [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": 11
        }
      ],
      "source": [
        "working_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tA6OdejvspOE"
      },
      "source": [
        "##Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "4VgaMWqDqSxZ"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "q2BDi41ssrHp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91cd421a-3999-4376-d12f-33b6a87971e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 1.80320e+04,\n",
              "        0.00000e+00, 1.60000e+01],\n",
              "       [3.32000e+02, 2.24000e+02, 3.33000e+03, ..., 1.15600e+03,\n",
              "        7.20000e+01, 1.80000e+01],\n",
              "       [3.32000e+02, 2.24000e+02, 3.33000e+03, ..., 2.70376e+05,\n",
              "        7.20000e+01, 1.80000e+01],\n",
              "       ...,\n",
              "       [3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 2.26480e+04,\n",
              "        7.20000e+01, 1.40000e+01],\n",
              "       [3.32000e+02, 2.24000e+02, 3.31660e+04, ..., 2.21600e+03,\n",
              "        0.00000e+00, 0.00000e+00],\n",
              "       [3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 3.18464e+05,\n",
              "        7.20000e+01, 0.00000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "data_in = data.drop(['Name','md5','legitimate'],axis=1)\n",
        "labels = data['legitimate'].values\n",
        "data_in\n",
        "data_in_val = data_in.values\n",
        "data_in_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "dmQ26B2ntHQ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e587f19a-5467-4916-d853-c6c1f29cf2d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "qfIVANqhtNI2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b22fdc9e-a2fa-4e77-dc14-c90215b7901d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.32000000e+02, 2.24000000e+02, 2.58000000e+02, 4.19430400e+06,\n",
              "       0.00000000e+00, 1.00000000e+00, 1.60000000e+01, 1.02400000e+03,\n",
              "       1.04857600e+06, 5.76680655e+00, 7.22105073e+00, 2.56884382e+00,\n",
              "       3.53793936e+00, 1.60000000e+01])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "extratrees = ExtraTreesClassifier().fit(data_in_val,labels)\n",
        "select = SelectFromModel(extratrees,prefit=True)#basically describing that we have to erform transform method on it \n",
        "data_in_val_new = select.transform(data_in_val)\n",
        "data_in_val_new.shape\n",
        "data_in_val.shape\n",
        "data_in_val_new[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "EluVuqJI-rl2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4e9145e-ce3b-4104-d2ee-115a17d79f78"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          1024\n",
              "1         33088\n",
              "2         32832\n",
              "3         33088\n",
              "4         33088\n",
              "          ...  \n",
              "138042    33088\n",
              "138043    32768\n",
              "138044    33088\n",
              "138045        0\n",
              "138046    33088\n",
              "Name: DllCharacteristics, Length: 138047, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "data['DllCharacteristics']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "UElBCcMPug-w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92c24b36-484a-4ed7-dcc1-5fcfc9871adf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 DllCharacteristics 0.15984389075715771\n",
            "2 Machine 0.13699253972773387\n",
            "3 Characteristics 0.09252833841736832\n",
            "4 SectionsMaxEntropy 0.06453720785730044\n",
            "5 VersionInformationSize 0.06032160130380414\n",
            "6 Subsystem 0.053292593157020736\n",
            "7 ImageBase 0.05104979751722236\n",
            "8 MajorSubsystemVersion 0.04482121958904673\n",
            "9 ResourcesMaxEntropy 0.03555229017938129\n",
            "10 SizeOfStackReserve 0.02875264634459458\n",
            "11 ResourcesMinEntropy 0.027917788787382717\n",
            "12 SizeOfOptionalHeader 0.026646613347678306\n",
            "13 MajorOperatingSystemVersion 0.024497266345672602\n",
            "14 SectionsMeanEntropy 0.019169149144893444\n"
          ]
        }
      ],
      "source": [
        "\"\"\"Calculating importance value \"\"\"\n",
        "features = data_in_val_new.shape[1]\n",
        "importances = extratrees.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "for f in range(features):\n",
        "  print(\"%d\"%(f+1),data.columns[2+indices[f]],importances[indices[f]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Mt8AOJPqxeXy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40347539-ed7b-4bfa-d5f9-ba0bc3fbc6f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 9.64000e+03,\n",
              "        7.20000e+01, 1.50000e+01],\n",
              "       [3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 9.64000e+03,\n",
              "        7.20000e+01, 1.50000e+01],\n",
              "       [3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 9.64000e+03,\n",
              "        7.20000e+01, 1.50000e+01],\n",
              "       ...,\n",
              "       [3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 3.75200e+03,\n",
              "        0.00000e+00, 2.30000e+01],\n",
              "       [3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 9.64000e+03,\n",
              "        0.00000e+00, 2.30000e+01],\n",
              "       [3.32000e+02, 2.24000e+02, 2.58000e+02, ..., 2.74432e+05,\n",
              "        7.20000e+01, 0.00000e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "legit_train,legit_test,train_output,test_output = train_test_split(data_in_val,labels,test_size=0.2)\n",
        "legit_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "SXm2yrgZKxI7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "498b906b-3102-4b4e-cbd8-2f001e0621ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "train_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "C68nedw2zHZ0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34c52728-58f8-4810-f90d-88c8a191e324"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "classify = RandomForestClassifier(n_estimators=50)\n",
        "classify.fit(legit_train,train_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "-WL1aE340J5q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f5b6242-1abb-48c0-dd8d-05992d13f0ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9955813111191597"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "classify.score(legit_test,test_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "nJ3PAWA_0fFu"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "result = classify.predict(legit_test)\n",
        "conf_mat = confusion_matrix(test_output,result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Eq4qV21e1HkL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7dd4b21-50f9-4c2b-df57-08172d501046"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[19304,    73],\n",
              "       [   49,  8184]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "conf_mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "5WAFsgADA-aV",
        "outputId": "da63f23f-f469-4d74-afee-bd901d71b780"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"0,0 =='true positive' 0,1=='false positive' 1,0='false negative' 1,1 =='true negative'\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ax = sns.heatmap(conf_mat,annot=True,fmt=\"d\", cmap=plt.cm.Blues, cbar=False)\n",
        "ax.set_xlabel('Predicted labels')\n",
        "ax.set_ylabel('True labels')\n",
        "\"\"\"0,0 =='true positive' 0,1=='false positive' 1,0='false negative' 1,1 =='true negative'\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ejCL5cre1RJC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d94c94c7-b9d2-4710-cf64-4174a0305c88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False positives:  0.003767353047427362\n",
            "False negative:  0.0059516579618608045\n"
          ]
        }
      ],
      "source": [
        "print(\"False positives: \",conf_mat[0][1]/sum(conf_mat[0]))\n",
        "print(\"False negative: \",conf_mat[1][0]/sum(conf_mat[1]))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Gradient Bossting Classifier"
      ],
      "metadata": {
        "id": "rMunPHG1EYC9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "AXr6BMss1xVZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee4fc9a2-4ecc-4716-ecc6-7124ccc8fa97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(n_estimators=50)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "\"\"\"\n",
        "Boosting \n",
        "\"\"\"\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "grad_boost = GradientBoostingClassifier(n_estimators=50)\n",
        "grad_boost.fit(legit_train,train_output)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "2NMysK8N5K9C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c93b9cff-6fe2-421c-915b-5f287f3d054d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.990220934444042"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "grad_boost.score(legit_test,test_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "_n7Sf21s5m4-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "0bf96397-7c65-4370-f60b-61d63c061f79"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Name  \\\n",
              "0                                       memtest.exe   \n",
              "1                                           ose.exe   \n",
              "2                                         setup.exe   \n",
              "3                                          DW20.EXE   \n",
              "4                                      dwtrig20.exe   \n",
              "...                                             ...   \n",
              "138042  VirusShare_8e292b418568d6e7b87f2a32aee7074b   \n",
              "138043  VirusShare_260d9e2258aed4c8a3bbd703ec895822   \n",
              "138044  VirusShare_8d088a51b7d225c9f5d11d239791ec3f   \n",
              "138045  VirusShare_4286dccf67ca220fe67635388229a9f3   \n",
              "138046  VirusShare_d7648eae45f09b3adb75127f43be6d11   \n",
              "\n",
              "                                     md5  Machine  SizeOfOptionalHeader  \\\n",
              "0       631ea355665f28d4707448e442fbf5b8      332                   224   \n",
              "1       9d10f99a6712e28f8acd5641e3a7ea6b      332                   224   \n",
              "2       4d92f518527353c0db88a70fddcfd390      332                   224   \n",
              "3       a41e524f8d45f0074fd07805ff0c9b12      332                   224   \n",
              "4       c87e561258f2f8650cef999bf643a731      332                   224   \n",
              "...                                  ...      ...                   ...   \n",
              "138042  8e292b418568d6e7b87f2a32aee7074b      332                   224   \n",
              "138043  260d9e2258aed4c8a3bbd703ec895822      332                   224   \n",
              "138044  8d088a51b7d225c9f5d11d239791ec3f      332                   224   \n",
              "138045  4286dccf67ca220fe67635388229a9f3      332                   224   \n",
              "138046  d7648eae45f09b3adb75127f43be6d11      332                   224   \n",
              "\n",
              "        Characteristics  MajorLinkerVersion  MinorLinkerVersion  SizeOfCode  \\\n",
              "0                   258                   9                   0      361984   \n",
              "1                  3330                   9                   0      130560   \n",
              "2                  3330                   9                   0      517120   \n",
              "3                   258                   9                   0      585728   \n",
              "4                   258                   9                   0      294912   \n",
              "...                 ...                 ...                 ...         ...   \n",
              "138042              258                  11                   0      205824   \n",
              "138043            33167                   2                  25       37888   \n",
              "138044              258                  10                   0      118272   \n",
              "138045            33166                   2                  25       49152   \n",
              "138046              258                  11                   0      111616   \n",
              "\n",
              "        SizeOfInitializedData  SizeOfUninitializedData  ...  ResourcesNb  \\\n",
              "0                      115712                        0  ...            4   \n",
              "1                       19968                        0  ...            2   \n",
              "2                      621568                        0  ...           11   \n",
              "3                      369152                        0  ...           10   \n",
              "4                      247296                        0  ...            2   \n",
              "...                       ...                      ...  ...          ...   \n",
              "138042                 223744                        0  ...            7   \n",
              "138043                 185344                        0  ...           26   \n",
              "138044                 380416                        0  ...           22   \n",
              "138045                  16896                        0  ...           10   \n",
              "138046                 468480                        0  ...            4   \n",
              "\n",
              "        ResourcesMeanEntropy  ResourcesMinEntropy  ResourcesMaxEntropy  \\\n",
              "0                   3.262823             2.568844             3.537939   \n",
              "1                   4.250461             3.420744             5.080177   \n",
              "2                   4.426324             2.846449             5.271813   \n",
              "3                   4.364291             2.669314             6.400720   \n",
              "4                   4.306100             3.421598             5.190603   \n",
              "...                      ...                  ...                  ...   \n",
              "138042              4.122736             1.370260             7.677091   \n",
              "138043              3.377663             2.031619             5.050074   \n",
              "138044              6.825406             2.617026             7.990487   \n",
              "138045              3.421627             2.060964             4.739744   \n",
              "138046              4.407252             1.980482             6.115374   \n",
              "\n",
              "        ResourcesMeanSize  ResourcesMinSize  ResourcesMaxSize  \\\n",
              "0             8797.000000               216             18032   \n",
              "1              837.000000               518              1156   \n",
              "2            31102.272727               104            270376   \n",
              "3             1457.000000                90              4264   \n",
              "4             1074.500000               849              1300   \n",
              "...                   ...               ...               ...   \n",
              "138042       14900.714286                16             81654   \n",
              "138043        6905.846154                44             67624   \n",
              "138044       14981.909091                48             22648   \n",
              "138045         601.600000                16              2216   \n",
              "138046       96625.000000                20            318464   \n",
              "\n",
              "        LoadConfigurationSize  VersionInformationSize  legitimate  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##ANN Classifier"
      ],
      "metadata": {
        "id": "BSx5c88VElfk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"legitimate\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ploYepd4EnP0",
        "outputId": "c8df5601-1c74-4044-9d43-ed9afe90a51c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    96724\n",
              "1    41323\n",
              "Name: legitimate, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data['legitimate'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "m2ZBTpSmFKQl",
        "outputId": "e4dffb83-ccd8-404e-e4fa-203b0a45482f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdf6a85a90>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corrMatrix = data.corr()\n",
        "sns.heatmap(corrMatrix,annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "lNti73yCFMhr",
        "outputId": "c159d6d3-bc5a-4f49-851c-3b08bd9c41bc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop([\"Name\",\"legitimate\",\"md5\"],axis=1)\n",
        "Y=data['legitimate']"
      ],
      "metadata": {
        "id": "b1efxyBPFVj8"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KrOJmovFZkc",
        "outputId": "9e58fcca-7586-478c-8fdd-e7f4fd2b2b2d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1\n",
              "1         1\n",
              "2         1\n",
              "3         1\n",
              "4         1\n",
              "         ..\n",
              "138042    0\n",
              "138043    0\n",
              "138044    0\n",
              "138045    0\n",
              "138046    0\n",
              "Name: legitimate, Length: 138047, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=1)"
      ],
      "metadata": {
        "id": "rUMevpDIFafM"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalizing the data\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler =StandardScaler()"
      ],
      "metadata": {
        "id": "ECFEWal9Fk0m"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "GlWIiKCsFuee"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kfHagS9TFxEe",
        "outputId": "0d14760f-42df-4d6b-cb4d-d79ccb4794d5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.35934973, -0.35865415, -0.51105104, ..., -0.0110814 ,\n",
              "        -0.01767443, -1.81442508],\n",
              "       [-0.35934973, -0.35865415,  0.487988  , ..., -0.0114918 ,\n",
              "        -0.01767443,  0.39014303],\n",
              "       [-0.35934973, -0.35865415,  0.48945144, ..., -0.01148065,\n",
              "        -0.01767769,  0.53711424],\n",
              "       ...,\n",
              "       [-0.35934973, -0.35865415, -0.51105104, ..., -0.0110814 ,\n",
              "        -0.01767443,  0.39014303],\n",
              "       [-0.35934973, -0.35865415,  0.487988  , ..., -0.0110814 ,\n",
              "        -0.01767443,  0.53711424],\n",
              "       [-0.35934973, -0.35865415, -0.51105104, ..., -0.01135037,\n",
              "        -0.01767769,  1.56591269]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "AUpwp-IfFyqG"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape[1]*2/3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1j5Yh1KNF2r-",
        "outputId": "770fe627-15d9-4dc7-eb22-5d8e4f544745"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36.0"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hidden_layer_size= 36\n",
        "model = Sequential()\n",
        "\n",
        "\n",
        "model.add(Dense(hidden_layer_size,input_dim=(x_train.shape[1]),activation='relu'))\n",
        "model.add(Dense(30,activation='relu'))\n",
        "model.add(Dense(26,activation='relu'))\n",
        "model.add(Dense(20,activation='relu'))\n",
        "model.add(Dense(16,activation='relu'))\n",
        "model.add(Dense(10,activation='relu'))\n",
        "model.add(Dense(8,activation='relu'))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNWFC0h-F8dX",
        "outputId": "50c6f2c7-8aed-4a41-8865-bf799d557137"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 36)                1980      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 30)                1110      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 26)                806       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 20)                540       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 16)                336       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 10)                170       \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 8)                 88        \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,039\n",
            "Trainable params: 5,039\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.compile(optimizer = 'adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.compile(optimizer='adam', \n",
        "              loss='binary_crossentropy', \n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "6YCjAgDWF_N-"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "max_epochs = 20\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(patience = 2)"
      ],
      "metadata": {
        "id": "_-k28H3hGEEu"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = model.fit(x=x_train,\n",
        "                   y=y_train,\n",
        "                   batch_size=batch_size,\n",
        "                   epochs=max_epochs,\n",
        "                   verbose=1,\n",
        "                   #callbacks=[early_stopping],\n",
        "                   validation_split=0.2)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1EuUn0PTGG0f",
        "outputId": "d4b48656-627d-4e55-f9fe-83facba64f21"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "2761/2761 [==============================] - 11s 3ms/step - loss: 0.0606 - accuracy: 0.9842 - val_loss: 0.0380 - val_accuracy: 0.9892\n",
            "Epoch 2/20\n",
            "2761/2761 [==============================] - 12s 4ms/step - loss: 0.0376 - accuracy: 0.9886 - val_loss: 0.0336 - val_accuracy: 0.9892\n",
            "Epoch 3/20\n",
            "2761/2761 [==============================] - 8s 3ms/step - loss: 0.0332 - accuracy: 0.9893 - val_loss: 0.0327 - val_accuracy: 0.9896\n",
            "Epoch 4/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0316 - accuracy: 0.9896 - val_loss: 0.0301 - val_accuracy: 0.9902\n",
            "Epoch 5/20\n",
            "2761/2761 [==============================] - 8s 3ms/step - loss: 0.0299 - accuracy: 0.9900 - val_loss: 0.0463 - val_accuracy: 0.9906\n",
            "Epoch 6/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0289 - accuracy: 0.9901 - val_loss: 0.0516 - val_accuracy: 0.9902\n",
            "Epoch 7/20\n",
            "2761/2761 [==============================] - 8s 3ms/step - loss: 0.0275 - accuracy: 0.9907 - val_loss: 0.0475 - val_accuracy: 0.9902\n",
            "Epoch 8/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0265 - accuracy: 0.9907 - val_loss: 0.0381 - val_accuracy: 0.9909\n",
            "Epoch 9/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0254 - accuracy: 0.9910 - val_loss: 0.0353 - val_accuracy: 0.9906\n",
            "Epoch 10/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0247 - accuracy: 0.9915 - val_loss: 0.0406 - val_accuracy: 0.9896\n",
            "Epoch 11/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0243 - accuracy: 0.9916 - val_loss: 0.0412 - val_accuracy: 0.9911\n",
            "Epoch 12/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0232 - accuracy: 0.9918 - val_loss: 0.0407 - val_accuracy: 0.9903\n",
            "Epoch 13/20\n",
            "2761/2761 [==============================] - 8s 3ms/step - loss: 0.0264 - accuracy: 0.9919 - val_loss: 0.0335 - val_accuracy: 0.9913\n",
            "Epoch 14/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0218 - accuracy: 0.9923 - val_loss: 0.0372 - val_accuracy: 0.9909\n",
            "Epoch 15/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0215 - accuracy: 0.9926 - val_loss: 0.0378 - val_accuracy: 0.9906\n",
            "Epoch 16/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0210 - accuracy: 0.9926 - val_loss: 0.0428 - val_accuracy: 0.9909\n",
            "Epoch 17/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0203 - accuracy: 0.9929 - val_loss: 0.0356 - val_accuracy: 0.9916\n",
            "Epoch 18/20\n",
            "2761/2761 [==============================] - 9s 3ms/step - loss: 0.0201 - accuracy: 0.9932 - val_loss: 0.0378 - val_accuracy: 0.9916\n",
            "Epoch 19/20\n",
            "2761/2761 [==============================] - 8s 3ms/step - loss: 0.0196 - accuracy: 0.9933 - val_loss: 0.0372 - val_accuracy: 0.9906\n",
            "Epoch 20/20\n",
            "2761/2761 [==============================] - 8s 3ms/step - loss: 0.0202 - accuracy: 0.9930 - val_loss: 0.0508 - val_accuracy: 0.9915\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fbd9e9edac0>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = result.history['accuracy']\n",
        "val_acc = result.history['val_accuracy']\n",
        "loss = result.history['loss']\n",
        "val_loss = result.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))\n",
        "sns.set_style(\"white\")\n",
        "plt.suptitle('Train history', size = 15)\n",
        "\n",
        "ax1.plot(epochs, acc, \"bo\", label = \"Training acc\")\n",
        "ax1.plot(epochs, val_acc, \"b\", label = \"Validation acc\")\n",
        "ax1.set_title(\"Training and validation acc\")\n",
        "ax1.legend()\n",
        "\n",
        "ax2.plot(epochs, loss, \"bo\", label = \"Training loss\", color = 'red')\n",
        "ax2.plot(epochs, val_loss, \"b\", label = \"Validation loss\", color = 'red')\n",
        "ax2.set_title(\"Training and validation loss\")\n",
        "ax2.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "qs0QvQlqGJoq",
        "outputId": "19e04294-5c36-4312-bc1b-0e5bff5be2de"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_path = \"saved_model\"\n",
        "tf.keras.models.save_model(model,model_path)"
      ],
      "metadata": {
        "id": "z71HkqJ7HJjU"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = tf.keras.models.load_model(model_path)"
      ],
      "metadata": {
        "id": "_Icf5Pq7HNzM"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1 = x_train[1]\n",
        "d2 = x_train[1000]\n"
      ],
      "metadata": {
        "id": "5-OEShDiH-7H"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHIaqqw0IBgw",
        "outputId": "281450a9-fe4c-4245-c088-849c002b9102"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(54,)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d1=d1.reshape(1,-1)\n",
        "d1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tItXVtKIDPg",
        "outputId": "6711fa88-1e64-488e-aa33-7b8a37ca71bc"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 54)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def scan(data):\n",
        "    d1 = data.reshape(1,-1)\n",
        "    loaded_model = tf.keras.models.load_model(model_path)\n",
        "    loaded_model.predict(d1)\n",
        "    result=round(loaded_model.predict(d1)[0][0])\n",
        "    if result == 0:\n",
        "        return \"Malware\"\n",
        "    else:\n",
        "        return \"Legitimate\""
      ],
      "metadata": {
        "id": "ckaHpkXcIGNw"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scan(d1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "-CFJ0eI7IKkA",
        "outputId": "366c31bc-3c28-47b3-efdd-9a65a871d5f9"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 189ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Legitimate'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scan(d2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "raTQw1bkINAY",
        "outputId": "47597951-7c27-4def-b924-b94dce47ee8f"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 261ms/step\n",
            "1/1 [==============================] - 0s 58ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Malware'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dIOAeEnlIROA"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}