Decision_Tree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3cec636e",
   "metadata": {},
   "source": [
    "# Malware Analysis With Machine Learning - Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2240e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\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": "f2253395",
   "metadata": {},
   "source": [
    "Data set reading :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7569420f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../Malware-Detection-Using-Machine-Learning/data-set/MalwareDataSet.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "206b4c35",
   "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",
       "      <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>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": "4b8b8b6c",
   "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": "65dbf455",
   "metadata": {
    "scrolled": true
   },
   "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": "7378e7cf",
   "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": "cea0ed83",
   "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": "0d9d18ee",
   "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": "dfb50f62",
   "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": "f323a0b1",
   "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": "9006bce3",
   "metadata": {},
   "source": [
    "We will use 75% of our dataset for training and 25% for testing : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0657650f",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train, features_test, ifMalware_train, ifMalware_test = train_test_split(features, ifMalware, test_size=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4edcf3e5",
   "metadata": {},
   "source": [
    "Learning and Predict :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab2b2a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtModel = tree.DecisionTreeClassifier() # Defined the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d458930d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtModel.fit(features_train, ifMalware_train) # Provided training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ede2da4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtPredict = dtModel.predict(features_test) # Give the test data then call predict."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d67adc3",
   "metadata": {},
   "source": [
    "Result :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1faa7928",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled out of a total of 34361 test entries: 483\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
    "                                                                      (ifMalware_test != dtPredict).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "14c7dc90",
   "metadata": {},
   "outputs": [],
   "source": [
    "successRate = 100 * f1_score(ifMalware_test, dtPredict, average='micro') # Success rate calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85d3f74a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Success Rate was calculated as % : 98.59433660254359 with the Decision Tree.\n"
     ]
    }
   ],
   "source": [
    "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with the Decision Tree.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffda807e",
   "metadata": {},
   "source": [
    "Visualization :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6799629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x121e7a5b0>"
      ]
     },
     "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(dtModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "caaf81cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x121e7ab20>"
      ]
     },
     "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(dtModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a7d9cfc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x123144550>"
      ]
     },
     "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(dtModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cc50825",
   "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
}