K_Nearest_Neighbors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75878b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\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": "69b25522",
   "metadata": {},
   "source": [
    "Data set reading :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b54e1b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../Malware-Detection-Using-Machine-Learning/data-set/MalwareDataSet.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47c9ba1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>33088</td>\n",
       "      <td>262144</td>\n",
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       "      <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",
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       "    <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": "b13348ab",
   "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": "33292bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": "65dbf455",
   "metadata": {
    "scrolled": true
   },
   "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": "8592b076",
   "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": "3da1b6cd",
   "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": "46db9f27",
   "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": "c0e0d3db",
   "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": "773b9a86",
   "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": "30274115",
   "metadata": {},
   "outputs": [],
   "source": [
    "knModel = KNeighborsClassifier(n_neighbors=1) # Defined the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b6993294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knModel.fit(features_train, ifMalware_train) # Provided training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a413829",
   "metadata": {},
   "outputs": [],
   "source": [
    "knPredict = knModel.predict(features_test) # Give the test data then call predict."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d67adc3",
   "metadata": {},
   "source": [
    "Results :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "42f2a30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of mislabeled out of a total of 34361 test entries: 838\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
    "                                                                      (ifMalware_test != knPredict).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cbbfc4fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "successRate = 100 * f1_score(ifMalware_test, knPredict, average='micro') # Success rate calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "894b2b35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Success Rate was calculated as % : 97.56118855679404 with the K-Nearest-Neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with the K-Nearest-Neighbors\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffda807e",
   "metadata": {},
   "source": [
    "Visualization :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a1af503a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x123674df0>"
      ]
     },
     "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(knModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "396f5630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x123f4cd90>"
      ]
     },
     "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(knModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6aced8b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x12416bbe0>"
      ]
     },
     "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(knModel,features_test,ifMalware_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "038c4e87",
   "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
}