Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "918bf7bd",
   "metadata": {},
   "source": [
    "# Malware Analysis With Machine Learning - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "034fde8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import plot_confusion_matrix,plot_precision_recall_curve,plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6b661b",
   "metadata": {},
   "source": [
    "Data set reading :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c4e18ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../Malware-Detection-Using-Machine-Learning/data-set/MalwareDataSet.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "386e250f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AddressOfEntryPoint</th>\n",
       "      <th>MajorLinkerVersion</th>\n",
       "      <th>MajorImageVersion</th>\n",
       "      <th>MajorOperatingSystemVersion</th>\n",
       "      <th>DllCharacteristics</th>\n",
       "      <th>SizeOfStackReserve</th>\n",
       "      <th>NumberOfSections</th>\n",
       "      <th>ResourceSize</th>\n",
       "      <th>legitimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10407</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>33088</td>\n",
       "      <td>262144</td>\n",
       "      <td>4</td>\n",
       "      <td>952</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5354</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>33088</td>\n",
       "      <td>262144</td>\n",
       "      <td>4</td>\n",
       "      <td>952</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58807</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>33088</td>\n",
       "      <td>262144</td>\n",
       "      <td>4</td>\n",
       "      <td>136490</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25166</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>33088</td>\n",
       "      <td>262144</td>\n",
       "      <td>4</td>\n",
       "      <td>1940</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70387</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>33088</td>\n",
       "      <td>262144</td>\n",
       "      <td>4</td>\n",
       "      <td>83098</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137439</th>\n",
       "      <td>123291</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>33088</td>\n",
       "      <td>1048576</td>\n",
       "      <td>5</td>\n",
       "      <td>81654</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137440</th>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>32768</td>\n",
       "      <td>1048576</td>\n",
       "      <td>8</td>\n",
       "      <td>67624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137441</th>\n",
       "      <td>59610</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>33088</td>\n",
       "      <td>1048576</td>\n",
       "      <td>5</td>\n",
       "      <td>22648</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137442</th>\n",
       "      <td>51216</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1048576</td>\n",
       "      <td>8</td>\n",
       "      <td>2216</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137443</th>\n",
       "      <td>22731</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>33088</td>\n",
       "      <td>1048576</td>\n",
       "      <td>5</td>\n",
       "      <td>318464</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137444 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
       "0                     10407                   9                  6   \n",
       "1                      5354                   9                  6   \n",
       "2                     58807                   9                  6   \n",
       "3                     25166                   9                  6   \n",
       "4                     70387                   9                  6   \n",
       "...                     ...                 ...                ...   \n",
       "137439               123291                  11                  0   \n",
       "137440                40000                   2                  6   \n",
       "137441                59610                  10                  0   \n",
       "137442                51216                   2                  0   \n",
       "137443                22731                  11                  0   \n",
       "\n",
       "        MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
       "0                                 6               33088              262144   \n",
       "1                                 6               33088              262144   \n",
       "2                                 6               33088              262144   \n",
       "3                                 6               33088              262144   \n",
       "4                                 6               33088              262144   \n",
       "...                             ...                 ...                 ...   \n",
       "137439                            5               33088             1048576   \n",
       "137440                            1               32768             1048576   \n",
       "137441                            5               33088             1048576   \n",
       "137442                            1                   0             1048576   \n",
       "137443                            5               33088             1048576   \n",
       "\n",
       "        NumberOfSections  ResourceSize  legitimate  \n",
       "0                      4           952           1  \n",
       "1                      4           952           1  \n",
       "2                      4        136490           1  \n",
       "3                      4          1940           1  \n",
       "4                      4         83098           1  \n",
       "...                  ...           ...         ...  \n",
       "137439                 5         81654           0  \n",
       "137440                 8         67624           0  \n",
       "137441                 5         22648           0  \n",
       "137442                 8          2216           0  \n",
       "137443                 5        318464           0  \n",
       "\n",
       "[137444 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd389773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(137444, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c146794",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AddressOfEntryPoint</th>\n",
       "      <th>MajorLinkerVersion</th>\n",
       "      <th>MajorImageVersion</th>\n",
       "      <th>MajorOperatingSystemVersion</th>\n",
       "      <th>DllCharacteristics</th>\n",
       "      <th>SizeOfStackReserve</th>\n",
       "      <th>NumberOfSections</th>\n",
       "      <th>ResourceSize</th>\n",
       "      <th>legitimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.374440e+05</td>\n",
       "      <td>137444.000000</td>\n",
       "      <td>137444.000000</td>\n",
       "      <td>137444.000000</td>\n",
       "      <td>137444.000000</td>\n",
       "      <td>1.374440e+05</td>\n",
       "      <td>137444.000000</td>\n",
       "      <td>1.374440e+05</td>\n",
       "      <td>137444.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.722186e+05</td>\n",
       "      <td>8.620784</td>\n",
       "      <td>68.731876</td>\n",
       "      <td>5.098738</td>\n",
       "      <td>22301.043436</td>\n",
       "      <td>9.306841e+05</td>\n",
       "      <td>4.997119</td>\n",
       "      <td>2.474766e+05</td>\n",
       "      <td>0.297707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.438014e+06</td>\n",
       "      <td>4.095635</td>\n",
       "      <td>1185.709873</td>\n",
       "      <td>99.437584</td>\n",
       "      <td>15444.753219</td>\n",
       "      <td>5.553175e+05</td>\n",
       "      <td>1.917237</td>\n",
       "      <td>2.129516e+07</td>\n",
       "      <td>0.457252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.272100e+04</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>1.048576e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.216000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.300800e+04</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>33088.000000</td>\n",
       "      <td>1.048576e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.640000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.157800e+04</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>33088.000000</td>\n",
       "      <td>1.048576e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.376250e+04</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.074484e+09</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>28619.000000</td>\n",
       "      <td>36868.000000</td>\n",
       "      <td>49504.000000</td>\n",
       "      <td>3.355443e+07</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.294903e+09</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
       "count         1.374440e+05       137444.000000      137444.000000   \n",
       "mean          1.722186e+05            8.620784          68.731876   \n",
       "std           3.438014e+06            4.095635        1185.709873   \n",
       "min           0.000000e+00            0.000000           0.000000   \n",
       "25%           1.272100e+04            8.000000           0.000000   \n",
       "50%           5.300800e+04            9.000000           0.000000   \n",
       "75%           6.157800e+04           10.000000           6.000000   \n",
       "max           1.074484e+09          255.000000       28619.000000   \n",
       "\n",
       "       MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
       "count                137444.000000       137444.000000        1.374440e+05   \n",
       "mean                      5.098738        22301.043436        9.306841e+05   \n",
       "std                      99.437584        15444.753219        5.553175e+05   \n",
       "min                       0.000000            0.000000        0.000000e+00   \n",
       "25%                       4.000000          320.000000        1.048576e+06   \n",
       "50%                       5.000000        33088.000000        1.048576e+06   \n",
       "75%                       5.000000        33088.000000        1.048576e+06   \n",
       "max                   36868.000000        49504.000000        3.355443e+07   \n",
       "\n",
       "       NumberOfSections  ResourceSize     legitimate  \n",
       "count     137444.000000  1.374440e+05  137444.000000  \n",
       "mean           4.997119  2.474766e+05       0.297707  \n",
       "std            1.917237  2.129516e+07       0.457252  \n",
       "min            1.000000  0.000000e+00       0.000000  \n",
       "25%            4.000000  2.216000e+03       0.000000  \n",
       "50%            5.000000  9.640000e+03       0.000000  \n",
       "75%            5.000000  2.376250e+04       1.000000  \n",
       "max           40.000000  4.294903e+09       1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4294347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "legitimate\n",
       "0    96526\n",
       "1    40918\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(data['legitimate']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eab99e9",
   "metadata": {},
   "source": [
    "We have a total of 137.444 data. There are 96.526 safe and 40.918 malware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b87ac400",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data.iloc[:,[0,1,2,3,4,5,6,7]].values # extracting the first 8 columns from the dataset - features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7dcb27ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  10407,       9,       6, ...,  262144,       4,     952],\n",
       "       [   5354,       9,       6, ...,  262144,       4,     952],\n",
       "       [  58807,       9,       6, ...,  262144,       4,  136490],\n",
       "       ...,\n",
       "       [  59610,      10,       0, ..., 1048576,       5,   22648],\n",
       "       [  51216,       2,       0, ..., 1048576,       8,    2216],\n",
       "       [  22731,      11,       0, ..., 1048576,       5,  318464]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "137e5f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ifMalware = data.iloc[:,8].values # extracting the legitimate column of the dataset - safe & malware results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52df4a9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ifMalware"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc0b019",
   "metadata": {},
   "source": [
    "We will use 75% of our dataset for training and 25% for testing :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a988b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train, features_test, ifMalware_train, ifMalware_test = train_test_split(features, ifMalware, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834ffef4",
   "metadata": {},
   "source": [
    "Learning and Predict :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f18cafe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfModel = RandomForestClassifier() # Defined the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8b11bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfModel.fit(features_train, ifMalware_train) # Provided training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4e41403c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfPredict = rfModel.predict(features_test) # Give the test data then call predict."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a29bb3",
   "metadata": {},
   "source": [
    "Results :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "310395c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled out of a total of 34361 test entries: 301\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
    "                                                                      (ifMalware_test != rfPredict).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3da4e570",
   "metadata": {},
   "outputs": [],
   "source": [
    "successRate = 100 * f1_score(ifMalware_test, rfPredict, average='micro') # Success rate calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a79bdd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Success Rate was calculated as % : 99.1240068682518 with the Random Forest\n"
     ]
    }
   ],
   "source": [
    "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with the Random Forest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc87041f",
   "metadata": {},
   "source": [
    "Visualization :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b5fb046a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x120a61af0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rfModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "326f5f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x103fdb460>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_precision_recall_curve(rfModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "61719e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x121c11fd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(rfModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ec51c31",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}