├── .gitattributes
├── Data_Set_Generator .ipynb
├── Decision_Tree.ipynb
├── K_Nearest_Neighbors.ipynb
├── LICENSE
├── README.md
├── Random_Forest.ipynb
└── data-set
    ├── MalwareDataSet.csv
    └── dataset.csv


/.gitattributes:
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1 | # Auto detect text files and perform LF normalization
2 | * text=auto
3 | 


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/Data_Set_Generator .ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "f51341ce",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Data Set Generator "
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": null,
 14 |    "id": "38f87734",
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import pefile\n",
 19 |     "import csv\n",
 20 |     "import glob"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": null,
 26 |    "id": "93abc748",
 27 |    "metadata": {},
 28 |    "outputs": [],
 29 |    "source": [
 30 |     "# pefile : It is used to extract pe information of exe files.\n",
 31 |     "# csv : Used to create csv file.\n",
 32 |     "# glob : It is used to access folders."
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "markdown",
 37 |    "id": "3de03b84",
 38 |    "metadata": {},
 39 |    "source": [
 40 |     "First create 2 folders, add your malwares in one folder and your safe softwares in the other folder. Then access the folders with glob as follows."
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": null,
 46 |    "id": "26392386",
 47 |    "metadata": {},
 48 |    "outputs": [],
 49 |    "source": [
 50 |     "malware = glob.glob('../malwares_folder//*.exe') # change path"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": null,
 56 |    "id": "e0833f40",
 57 |    "metadata": {},
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "secure = glob.glob('../secures_folder//*.exe') # change path"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "markdown",
 65 |    "id": "70f615e1",
 66 |    "metadata": {},
 67 |    "source": [
 68 |     "Define header information in dataset"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "code",
 73 |    "execution_count": null,
 74 |    "id": "f2111f89",
 75 |    "metadata": {},
 76 |    "outputs": [],
 77 |    "source": [
 78 |     "header =[\"AddressOfEntryPoint\",\"MajorLinkerVersion\",\"MajorImageVersion\",\"MajorOperatingSystemVersion\",\"DllCharacteristics\",\"SizeOfStackReserve\",\"NumberOfSections\",\"ResourceSize\",\"IfMalware\"]"
 79 |    ]
 80 |   },
 81 |   {
 82 |    "cell_type": "markdown",
 83 |    "id": "9726a7e4",
 84 |    "metadata": {},
 85 |    "source": [
 86 |     "Use the following code structure to generate dataset in csv file format:"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": null,
 92 |    "id": "a098d0b9",
 93 |    "metadata": {},
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "with open('dataset.csv', 'w', encoding='UTF8', newline='') as f:\n",
 97 |     "    writer = csv.writer(f)\n",
 98 |     "\n",
 99 |     "    writer.writerow(header) # header info added\n",
100 |     "\n",
101 |     "    # We added the pe information for Malware folder:\n",
102 |     "    \n",
103 |     "        pe = pefile.PE(file)\n",
104 |     "        a = str(pe.OPTIONAL_HEADER.AddressOfEntryPoint)\n",
105 |     "        b = str(pe.OPTIONAL_HEADER.MajorLinkerVersion)\n",
106 |     "        c = str(pe.OPTIONAL_HEADER.MajorImageVersion)\n",
107 |     "        d = str(pe.OPTIONAL_HEADER.MajorOperatingSystemVersion)\n",
108 |     "        e = str(pe.OPTIONAL_HEADER.DllCharacteristics)\n",
109 |     "        f = str(pe.OPTIONAL_HEADER.SizeOfStackReserve)\n",
110 |     "        g = str(pe.FILE_HEADER.NumberOfSections)\n",
111 |     "        h = str(pe.OPTIONAL_HEADER.DATA_DIRECTORY[2].Size)\n",
112 |     "        i = \"1\" # dengerous exe file value\n",
113 |     "        \n",
114 |     "        data = [a,b,c,d,e,f,g,h,i]\n",
115 |     "        writer.writerow(data)\n",
116 |     "    \n",
117 |     "    # We added the pe information for safe software folder:\n",
118 |     "    \n",
119 |     "    for file in secure:\n",
120 |     "        pe = pefile.PE(file)\n",
121 |     "        a = str(pe.OPTIONAL_HEADER.AddressOfEntryPoint)\n",
122 |     "        b = str(pe.OPTIONAL_HEADER.MajorLinkerVersion)\n",
123 |     "        c = str(pe.OPTIONAL_HEADER.MajorImageVersion)\n",
124 |     "        d = str(pe.OPTIONAL_HEADER.MajorOperatingSystemVersion)\n",
125 |     "        e = str(pe.OPTIONAL_HEADER.DllCharacteristics)\n",
126 |     "        f = str(pe.OPTIONAL_HEADER.SizeOfStackReserve)\n",
127 |     "        g = str(pe.FILE_HEADER.NumberOfSections)\n",
128 |     "        h = str(pe.OPTIONAL_HEADER.DATA_DIRECTORY[2].Size)\n",
129 |     "        i = \"0\" # safe exe file value\n",
130 |     "        \n",
131 |     "        data = [a,b,c,d,e,f,g,h,i]\n",
132 |     "        writer.writerow(data)"
133 |    ]
134 |   },
135 |   {
136 |    "cell_type": "markdown",
137 |    "id": "15004a13",
138 |    "metadata": {},
139 |    "source": [
140 |     "We created our own dataset by extracting the PE information of the exe files and writing them to the csv file."
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": null,
146 |    "id": "30181e79",
147 |    "metadata": {},
148 |    "outputs": [],
149 |    "source": []
150 |   }
151 |  ],
152 |  "metadata": {
153 |   "kernelspec": {
154 |    "display_name": "Python 3 (ipykernel)",
155 |    "language": "python",
156 |    "name": "python3"
157 |   },
158 |   "language_info": {
159 |    "codemirror_mode": {
160 |     "name": "ipython",
161 |     "version": 3
162 |    },
163 |    "file_extension": ".py",
164 |    "mimetype": "text/x-python",
165 |    "name": "python",
166 |    "nbconvert_exporter": "python",
167 |    "pygments_lexer": "ipython3",
168 |    "version": "3.9.10"
169 |   }
170 |  },
171 |  "nbformat": 4,
172 |  "nbformat_minor": 5
173 | }
174 | 


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/Decision_Tree.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "3cec636e",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Malware Analysis With Machine Learning - Decision Tree"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": 1,
 14 |    "id": "c2240e7d",
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import numpy as np\n",
 19 |     "import pandas as pd\n",
 20 |     "from sklearn.model_selection import train_test_split\n",
 21 |     "from sklearn import tree\n",
 22 |     "from sklearn.metrics import f1_score\n",
 23 |     "from sklearn.metrics import plot_confusion_matrix,plot_precision_recall_curve,plot_roc_curve"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "markdown",
 28 |    "id": "f2253395",
 29 |    "metadata": {},
 30 |    "source": [
 31 |     "Data set reading :"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 2,
 37 |    "id": "7569420f",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "data = pd.read_csv('../Malware-Detection-Using-Machine-Learning/data-set/MalwareDataSet.csv') "
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 3,
 47 |    "id": "206b4c35",
 48 |    "metadata": {},
 49 |    "outputs": [
 50 |     {
 51 |      "data": {
 52 |       "text/html": [
 53 |        "<div>\n",
 54 |        "<style scoped>\n",
 55 |        "    .dataframe tbody tr th:only-of-type {\n",
 56 |        "        vertical-align: middle;\n",
 57 |        "    }\n",
 58 |        "\n",
 59 |        "    .dataframe tbody tr th {\n",
 60 |        "        vertical-align: top;\n",
 61 |        "    }\n",
 62 |        "\n",
 63 |        "    .dataframe thead th {\n",
 64 |        "        text-align: right;\n",
 65 |        "    }\n",
 66 |        "</style>\n",
 67 |        "<table border=\"1\" class=\"dataframe\">\n",
 68 |        "  <thead>\n",
 69 |        "    <tr style=\"text-align: right;\">\n",
 70 |        "      <th></th>\n",
 71 |        "      <th>AddressOfEntryPoint</th>\n",
 72 |        "      <th>MajorLinkerVersion</th>\n",
 73 |        "      <th>MajorImageVersion</th>\n",
 74 |        "      <th>MajorOperatingSystemVersion</th>\n",
 75 |        "      <th>DllCharacteristics</th>\n",
 76 |        "      <th>SizeOfStackReserve</th>\n",
 77 |        "      <th>NumberOfSections</th>\n",
 78 |        "      <th>ResourceSize</th>\n",
 79 |        "      <th>legitimate</th>\n",
 80 |        "    </tr>\n",
 81 |        "  </thead>\n",
 82 |        "  <tbody>\n",
 83 |        "    <tr>\n",
 84 |        "      <th>0</th>\n",
 85 |        "      <td>10407</td>\n",
 86 |        "      <td>9</td>\n",
 87 |        "      <td>6</td>\n",
 88 |        "      <td>6</td>\n",
 89 |        "      <td>33088</td>\n",
 90 |        "      <td>262144</td>\n",
 91 |        "      <td>4</td>\n",
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 93 |        "      <td>1</td>\n",
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104 |        "      <td>952</td>\n",
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106 |        "    </tr>\n",
107 |        "    <tr>\n",
108 |        "      <th>2</th>\n",
109 |        "      <td>58807</td>\n",
110 |        "      <td>9</td>\n",
111 |        "      <td>6</td>\n",
112 |        "      <td>6</td>\n",
113 |        "      <td>33088</td>\n",
114 |        "      <td>262144</td>\n",
115 |        "      <td>4</td>\n",
116 |        "      <td>136490</td>\n",
117 |        "      <td>1</td>\n",
118 |        "    </tr>\n",
119 |        "    <tr>\n",
120 |        "      <th>3</th>\n",
121 |        "      <td>25166</td>\n",
122 |        "      <td>9</td>\n",
123 |        "      <td>6</td>\n",
124 |        "      <td>6</td>\n",
125 |        "      <td>33088</td>\n",
126 |        "      <td>262144</td>\n",
127 |        "      <td>4</td>\n",
128 |        "      <td>1940</td>\n",
129 |        "      <td>1</td>\n",
130 |        "    </tr>\n",
131 |        "    <tr>\n",
132 |        "      <th>4</th>\n",
133 |        "      <td>70387</td>\n",
134 |        "      <td>9</td>\n",
135 |        "      <td>6</td>\n",
136 |        "      <td>6</td>\n",
137 |        "      <td>33088</td>\n",
138 |        "      <td>262144</td>\n",
139 |        "      <td>4</td>\n",
140 |        "      <td>83098</td>\n",
141 |        "      <td>1</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>...</th>\n",
145 |        "      <td>...</td>\n",
146 |        "      <td>...</td>\n",
147 |        "      <td>...</td>\n",
148 |        "      <td>...</td>\n",
149 |        "      <td>...</td>\n",
150 |        "      <td>...</td>\n",
151 |        "      <td>...</td>\n",
152 |        "      <td>...</td>\n",
153 |        "      <td>...</td>\n",
154 |        "    </tr>\n",
155 |        "    <tr>\n",
156 |        "      <th>137439</th>\n",
157 |        "      <td>123291</td>\n",
158 |        "      <td>11</td>\n",
159 |        "      <td>0</td>\n",
160 |        "      <td>5</td>\n",
161 |        "      <td>33088</td>\n",
162 |        "      <td>1048576</td>\n",
163 |        "      <td>5</td>\n",
164 |        "      <td>81654</td>\n",
165 |        "      <td>0</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>137440</th>\n",
169 |        "      <td>40000</td>\n",
170 |        "      <td>2</td>\n",
171 |        "      <td>6</td>\n",
172 |        "      <td>1</td>\n",
173 |        "      <td>32768</td>\n",
174 |        "      <td>1048576</td>\n",
175 |        "      <td>8</td>\n",
176 |        "      <td>67624</td>\n",
177 |        "      <td>0</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>137441</th>\n",
181 |        "      <td>59610</td>\n",
182 |        "      <td>10</td>\n",
183 |        "      <td>0</td>\n",
184 |        "      <td>5</td>\n",
185 |        "      <td>33088</td>\n",
186 |        "      <td>1048576</td>\n",
187 |        "      <td>5</td>\n",
188 |        "      <td>22648</td>\n",
189 |        "      <td>0</td>\n",
190 |        "    </tr>\n",
191 |        "    <tr>\n",
192 |        "      <th>137442</th>\n",
193 |        "      <td>51216</td>\n",
194 |        "      <td>2</td>\n",
195 |        "      <td>0</td>\n",
196 |        "      <td>1</td>\n",
197 |        "      <td>0</td>\n",
198 |        "      <td>1048576</td>\n",
199 |        "      <td>8</td>\n",
200 |        "      <td>2216</td>\n",
201 |        "      <td>0</td>\n",
202 |        "    </tr>\n",
203 |        "    <tr>\n",
204 |        "      <th>137443</th>\n",
205 |        "      <td>22731</td>\n",
206 |        "      <td>11</td>\n",
207 |        "      <td>0</td>\n",
208 |        "      <td>5</td>\n",
209 |        "      <td>33088</td>\n",
210 |        "      <td>1048576</td>\n",
211 |        "      <td>5</td>\n",
212 |        "      <td>318464</td>\n",
213 |        "      <td>0</td>\n",
214 |        "    </tr>\n",
215 |        "  </tbody>\n",
216 |        "</table>\n",
217 |        "<p>137444 rows × 9 columns</p>\n",
218 |        "</div>"
219 |       ],
220 |       "text/plain": [
221 |        "        AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
222 |        "0                     10407                   9                  6   \n",
223 |        "1                      5354                   9                  6   \n",
224 |        "2                     58807                   9                  6   \n",
225 |        "3                     25166                   9                  6   \n",
226 |        "4                     70387                   9                  6   \n",
227 |        "...                     ...                 ...                ...   \n",
228 |        "137439               123291                  11                  0   \n",
229 |        "137440                40000                   2                  6   \n",
230 |        "137441                59610                  10                  0   \n",
231 |        "137442                51216                   2                  0   \n",
232 |        "137443                22731                  11                  0   \n",
233 |        "\n",
234 |        "        MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
235 |        "0                                 6               33088              262144   \n",
236 |        "1                                 6               33088              262144   \n",
237 |        "2                                 6               33088              262144   \n",
238 |        "3                                 6               33088              262144   \n",
239 |        "4                                 6               33088              262144   \n",
240 |        "...                             ...                 ...                 ...   \n",
241 |        "137439                            5               33088             1048576   \n",
242 |        "137440                            1               32768             1048576   \n",
243 |        "137441                            5               33088             1048576   \n",
244 |        "137442                            1                   0             1048576   \n",
245 |        "137443                            5               33088             1048576   \n",
246 |        "\n",
247 |        "        NumberOfSections  ResourceSize  legitimate  \n",
248 |        "0                      4           952           1  \n",
249 |        "1                      4           952           1  \n",
250 |        "2                      4        136490           1  \n",
251 |        "3                      4          1940           1  \n",
252 |        "4                      4         83098           1  \n",
253 |        "...                  ...           ...         ...  \n",
254 |        "137439                 5         81654           0  \n",
255 |        "137440                 8         67624           0  \n",
256 |        "137441                 5         22648           0  \n",
257 |        "137442                 8          2216           0  \n",
258 |        "137443                 5        318464           0  \n",
259 |        "\n",
260 |        "[137444 rows x 9 columns]"
261 |       ]
262 |      },
263 |      "execution_count": 3,
264 |      "metadata": {},
265 |      "output_type": "execute_result"
266 |     }
267 |    ],
268 |    "source": [
269 |     "data"
270 |    ]
271 |   },
272 |   {
273 |    "cell_type": "code",
274 |    "execution_count": 4,
275 |    "id": "4b8b8b6c",
276 |    "metadata": {},
277 |    "outputs": [
278 |     {
279 |      "data": {
280 |       "text/plain": [
281 |        "(137444, 9)"
282 |       ]
283 |      },
284 |      "execution_count": 4,
285 |      "metadata": {},
286 |      "output_type": "execute_result"
287 |     }
288 |    ],
289 |    "source": [
290 |     "data.shape"
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": 5,
296 |    "id": "65dbf455",
297 |    "metadata": {
298 |     "scrolled": true
299 |    },
300 |    "outputs": [
301 |     {
302 |      "data": {
303 |       "text/html": [
304 |        "<div>\n",
305 |        "<style scoped>\n",
306 |        "    .dataframe tbody tr th:only-of-type {\n",
307 |        "        vertical-align: middle;\n",
308 |        "    }\n",
309 |        "\n",
310 |        "    .dataframe tbody tr th {\n",
311 |        "        vertical-align: top;\n",
312 |        "    }\n",
313 |        "\n",
314 |        "    .dataframe thead th {\n",
315 |        "        text-align: right;\n",
316 |        "    }\n",
317 |        "</style>\n",
318 |        "<table border=\"1\" class=\"dataframe\">\n",
319 |        "  <thead>\n",
320 |        "    <tr style=\"text-align: right;\">\n",
321 |        "      <th></th>\n",
322 |        "      <th>AddressOfEntryPoint</th>\n",
323 |        "      <th>MajorLinkerVersion</th>\n",
324 |        "      <th>MajorImageVersion</th>\n",
325 |        "      <th>MajorOperatingSystemVersion</th>\n",
326 |        "      <th>DllCharacteristics</th>\n",
327 |        "      <th>SizeOfStackReserve</th>\n",
328 |        "      <th>NumberOfSections</th>\n",
329 |        "      <th>ResourceSize</th>\n",
330 |        "      <th>legitimate</th>\n",
331 |        "    </tr>\n",
332 |        "  </thead>\n",
333 |        "  <tbody>\n",
334 |        "    <tr>\n",
335 |        "      <th>count</th>\n",
336 |        "      <td>1.374440e+05</td>\n",
337 |        "      <td>137444.000000</td>\n",
338 |        "      <td>137444.000000</td>\n",
339 |        "      <td>137444.000000</td>\n",
340 |        "      <td>137444.000000</td>\n",
341 |        "      <td>1.374440e+05</td>\n",
342 |        "      <td>137444.000000</td>\n",
343 |        "      <td>1.374440e+05</td>\n",
344 |        "      <td>137444.000000</td>\n",
345 |        "    </tr>\n",
346 |        "    <tr>\n",
347 |        "      <th>mean</th>\n",
348 |        "      <td>1.722186e+05</td>\n",
349 |        "      <td>8.620784</td>\n",
350 |        "      <td>68.731876</td>\n",
351 |        "      <td>5.098738</td>\n",
352 |        "      <td>22301.043436</td>\n",
353 |        "      <td>9.306841e+05</td>\n",
354 |        "      <td>4.997119</td>\n",
355 |        "      <td>2.474766e+05</td>\n",
356 |        "      <td>0.297707</td>\n",
357 |        "    </tr>\n",
358 |        "    <tr>\n",
359 |        "      <th>std</th>\n",
360 |        "      <td>3.438014e+06</td>\n",
361 |        "      <td>4.095635</td>\n",
362 |        "      <td>1185.709873</td>\n",
363 |        "      <td>99.437584</td>\n",
364 |        "      <td>15444.753219</td>\n",
365 |        "      <td>5.553175e+05</td>\n",
366 |        "      <td>1.917237</td>\n",
367 |        "      <td>2.129516e+07</td>\n",
368 |        "      <td>0.457252</td>\n",
369 |        "    </tr>\n",
370 |        "    <tr>\n",
371 |        "      <th>min</th>\n",
372 |        "      <td>0.000000e+00</td>\n",
373 |        "      <td>0.000000</td>\n",
374 |        "      <td>0.000000</td>\n",
375 |        "      <td>0.000000</td>\n",
376 |        "      <td>0.000000</td>\n",
377 |        "      <td>0.000000e+00</td>\n",
378 |        "      <td>1.000000</td>\n",
379 |        "      <td>0.000000e+00</td>\n",
380 |        "      <td>0.000000</td>\n",
381 |        "    </tr>\n",
382 |        "    <tr>\n",
383 |        "      <th>25%</th>\n",
384 |        "      <td>1.272100e+04</td>\n",
385 |        "      <td>8.000000</td>\n",
386 |        "      <td>0.000000</td>\n",
387 |        "      <td>4.000000</td>\n",
388 |        "      <td>320.000000</td>\n",
389 |        "      <td>1.048576e+06</td>\n",
390 |        "      <td>4.000000</td>\n",
391 |        "      <td>2.216000e+03</td>\n",
392 |        "      <td>0.000000</td>\n",
393 |        "    </tr>\n",
394 |        "    <tr>\n",
395 |        "      <th>50%</th>\n",
396 |        "      <td>5.300800e+04</td>\n",
397 |        "      <td>9.000000</td>\n",
398 |        "      <td>0.000000</td>\n",
399 |        "      <td>5.000000</td>\n",
400 |        "      <td>33088.000000</td>\n",
401 |        "      <td>1.048576e+06</td>\n",
402 |        "      <td>5.000000</td>\n",
403 |        "      <td>9.640000e+03</td>\n",
404 |        "      <td>0.000000</td>\n",
405 |        "    </tr>\n",
406 |        "    <tr>\n",
407 |        "      <th>75%</th>\n",
408 |        "      <td>6.157800e+04</td>\n",
409 |        "      <td>10.000000</td>\n",
410 |        "      <td>6.000000</td>\n",
411 |        "      <td>5.000000</td>\n",
412 |        "      <td>33088.000000</td>\n",
413 |        "      <td>1.048576e+06</td>\n",
414 |        "      <td>5.000000</td>\n",
415 |        "      <td>2.376250e+04</td>\n",
416 |        "      <td>1.000000</td>\n",
417 |        "    </tr>\n",
418 |        "    <tr>\n",
419 |        "      <th>max</th>\n",
420 |        "      <td>1.074484e+09</td>\n",
421 |        "      <td>255.000000</td>\n",
422 |        "      <td>28619.000000</td>\n",
423 |        "      <td>36868.000000</td>\n",
424 |        "      <td>49504.000000</td>\n",
425 |        "      <td>3.355443e+07</td>\n",
426 |        "      <td>40.000000</td>\n",
427 |        "      <td>4.294903e+09</td>\n",
428 |        "      <td>1.000000</td>\n",
429 |        "    </tr>\n",
430 |        "  </tbody>\n",
431 |        "</table>\n",
432 |        "</div>"
433 |       ],
434 |       "text/plain": [
435 |        "       AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
436 |        "count         1.374440e+05       137444.000000      137444.000000   \n",
437 |        "mean          1.722186e+05            8.620784          68.731876   \n",
438 |        "std           3.438014e+06            4.095635        1185.709873   \n",
439 |        "min           0.000000e+00            0.000000           0.000000   \n",
440 |        "25%           1.272100e+04            8.000000           0.000000   \n",
441 |        "50%           5.300800e+04            9.000000           0.000000   \n",
442 |        "75%           6.157800e+04           10.000000           6.000000   \n",
443 |        "max           1.074484e+09          255.000000       28619.000000   \n",
444 |        "\n",
445 |        "       MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
446 |        "count                137444.000000       137444.000000        1.374440e+05   \n",
447 |        "mean                      5.098738        22301.043436        9.306841e+05   \n",
448 |        "std                      99.437584        15444.753219        5.553175e+05   \n",
449 |        "min                       0.000000            0.000000        0.000000e+00   \n",
450 |        "25%                       4.000000          320.000000        1.048576e+06   \n",
451 |        "50%                       5.000000        33088.000000        1.048576e+06   \n",
452 |        "75%                       5.000000        33088.000000        1.048576e+06   \n",
453 |        "max                   36868.000000        49504.000000        3.355443e+07   \n",
454 |        "\n",
455 |        "       NumberOfSections  ResourceSize     legitimate  \n",
456 |        "count     137444.000000  1.374440e+05  137444.000000  \n",
457 |        "mean           4.997119  2.474766e+05       0.297707  \n",
458 |        "std            1.917237  2.129516e+07       0.457252  \n",
459 |        "min            1.000000  0.000000e+00       0.000000  \n",
460 |        "25%            4.000000  2.216000e+03       0.000000  \n",
461 |        "50%            5.000000  9.640000e+03       0.000000  \n",
462 |        "75%            5.000000  2.376250e+04       1.000000  \n",
463 |        "max           40.000000  4.294903e+09       1.000000  "
464 |       ]
465 |      },
466 |      "execution_count": 5,
467 |      "metadata": {},
468 |      "output_type": "execute_result"
469 |     }
470 |    ],
471 |    "source": [
472 |     "data.describe()"
473 |    ]
474 |   },
475 |   {
476 |    "cell_type": "code",
477 |    "execution_count": 6,
478 |    "id": "7378e7cf",
479 |    "metadata": {},
480 |    "outputs": [
481 |     {
482 |      "data": {
483 |       "text/plain": [
484 |        "legitimate\n",
485 |        "0    96526\n",
486 |        "1    40918\n",
487 |        "dtype: int64"
488 |       ]
489 |      },
490 |      "execution_count": 6,
491 |      "metadata": {},
492 |      "output_type": "execute_result"
493 |     }
494 |    ],
495 |    "source": [
496 |     "data.groupby(data['legitimate']).size()"
497 |    ]
498 |   },
499 |   {
500 |    "cell_type": "markdown",
501 |    "id": "2eab99e9",
502 |    "metadata": {},
503 |    "source": [
504 |     "We have a total of 137.444 data. There are 96.526 safe and 40.918 malware."
505 |    ]
506 |   },
507 |   {
508 |    "cell_type": "code",
509 |    "execution_count": 7,
510 |    "id": "cea0ed83",
511 |    "metadata": {},
512 |    "outputs": [],
513 |    "source": [
514 |     "features = data.iloc[:,[0,1,2,3,4,5,6,7]].values # extracting the first 8 columns from the dataset - features"
515 |    ]
516 |   },
517 |   {
518 |    "cell_type": "code",
519 |    "execution_count": 8,
520 |    "id": "0d9d18ee",
521 |    "metadata": {},
522 |    "outputs": [
523 |     {
524 |      "data": {
525 |       "text/plain": [
526 |        "array([[  10407,       9,       6, ...,  262144,       4,     952],\n",
527 |        "       [   5354,       9,       6, ...,  262144,       4,     952],\n",
528 |        "       [  58807,       9,       6, ...,  262144,       4,  136490],\n",
529 |        "       ...,\n",
530 |        "       [  59610,      10,       0, ..., 1048576,       5,   22648],\n",
531 |        "       [  51216,       2,       0, ..., 1048576,       8,    2216],\n",
532 |        "       [  22731,      11,       0, ..., 1048576,       5,  318464]])"
533 |       ]
534 |      },
535 |      "execution_count": 8,
536 |      "metadata": {},
537 |      "output_type": "execute_result"
538 |     }
539 |    ],
540 |    "source": [
541 |     "features"
542 |    ]
543 |   },
544 |   {
545 |    "cell_type": "code",
546 |    "execution_count": 9,
547 |    "id": "dfb50f62",
548 |    "metadata": {},
549 |    "outputs": [],
550 |    "source": [
551 |     "ifMalware = data.iloc[:,8].values # extracting the legitimate column of the dataset - safe & malware results"
552 |    ]
553 |   },
554 |   {
555 |    "cell_type": "code",
556 |    "execution_count": 10,
557 |    "id": "f323a0b1",
558 |    "metadata": {},
559 |    "outputs": [
560 |     {
561 |      "data": {
562 |       "text/plain": [
563 |        "array([1, 1, 1, ..., 0, 0, 0])"
564 |       ]
565 |      },
566 |      "execution_count": 10,
567 |      "metadata": {},
568 |      "output_type": "execute_result"
569 |     }
570 |    ],
571 |    "source": [
572 |     "ifMalware"
573 |    ]
574 |   },
575 |   {
576 |    "cell_type": "markdown",
577 |    "id": "9006bce3",
578 |    "metadata": {},
579 |    "source": [
580 |     "We will use 75% of our dataset for training and 25% for testing : "
581 |    ]
582 |   },
583 |   {
584 |    "cell_type": "code",
585 |    "execution_count": 11,
586 |    "id": "0657650f",
587 |    "metadata": {},
588 |    "outputs": [],
589 |    "source": [
590 |     "features_train, features_test, ifMalware_train, ifMalware_test = train_test_split(features, ifMalware, test_size=0.25)"
591 |    ]
592 |   },
593 |   {
594 |    "cell_type": "markdown",
595 |    "id": "4edcf3e5",
596 |    "metadata": {},
597 |    "source": [
598 |     "Learning and Predict :"
599 |    ]
600 |   },
601 |   {
602 |    "cell_type": "code",
603 |    "execution_count": 12,
604 |    "id": "ab2b2a4a",
605 |    "metadata": {},
606 |    "outputs": [],
607 |    "source": [
608 |     "dtModel = tree.DecisionTreeClassifier() # Defined the model."
609 |    ]
610 |   },
611 |   {
612 |    "cell_type": "code",
613 |    "execution_count": 13,
614 |    "id": "d458930d",
615 |    "metadata": {},
616 |    "outputs": [
617 |     {
618 |      "data": {
619 |       "text/plain": [
620 |        "DecisionTreeClassifier()"
621 |       ]
622 |      },
623 |      "execution_count": 13,
624 |      "metadata": {},
625 |      "output_type": "execute_result"
626 |     }
627 |    ],
628 |    "source": [
629 |     "dtModel.fit(features_train, ifMalware_train) # Provided training data."
630 |    ]
631 |   },
632 |   {
633 |    "cell_type": "code",
634 |    "execution_count": 14,
635 |    "id": "5ede2da4",
636 |    "metadata": {},
637 |    "outputs": [],
638 |    "source": [
639 |     "dtPredict = dtModel.predict(features_test) # Give the test data then call predict."
640 |    ]
641 |   },
642 |   {
643 |    "cell_type": "markdown",
644 |    "id": "9d67adc3",
645 |    "metadata": {},
646 |    "source": [
647 |     "Result :"
648 |    ]
649 |   },
650 |   {
651 |    "cell_type": "code",
652 |    "execution_count": 15,
653 |    "id": "1faa7928",
654 |    "metadata": {},
655 |    "outputs": [
656 |     {
657 |      "name": "stdout",
658 |      "output_type": "stream",
659 |      "text": [
660 |       "Number of mislabeled out of a total of 34361 test entries: 483\n"
661 |      ]
662 |     }
663 |    ],
664 |    "source": [
665 |     "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
666 |     "                                                                      (ifMalware_test != dtPredict).sum()))"
667 |    ]
668 |   },
669 |   {
670 |    "cell_type": "code",
671 |    "execution_count": 16,
672 |    "id": "14c7dc90",
673 |    "metadata": {},
674 |    "outputs": [],
675 |    "source": [
676 |     "successRate = 100 * f1_score(ifMalware_test, dtPredict, average='micro') # Success rate calculation"
677 |    ]
678 |   },
679 |   {
680 |    "cell_type": "code",
681 |    "execution_count": 17,
682 |    "id": "85d3f74a",
683 |    "metadata": {},
684 |    "outputs": [
685 |     {
686 |      "name": "stdout",
687 |      "output_type": "stream",
688 |      "text": [
689 |       "The Success Rate was calculated as % : 98.59433660254359 with the Decision Tree.\n"
690 |      ]
691 |     }
692 |    ],
693 |    "source": [
694 |     "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with the Decision Tree.\")"
695 |    ]
696 |   },
697 |   {
698 |    "cell_type": "markdown",
699 |    "id": "ffda807e",
700 |    "metadata": {},
701 |    "source": [
702 |     "Visualization :"
703 |    ]
704 |   },
705 |   {
706 |    "cell_type": "code",
707 |    "execution_count": 18,
708 |    "id": "d6799629",
709 |    "metadata": {},
710 |    "outputs": [
711 |     {
712 |      "data": {
713 |       "text/plain": [
714 |        "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x121e7a5b0>"
715 |       ]
716 |      },
717 |      "execution_count": 18,
718 |      "metadata": {},
719 |      "output_type": "execute_result"
720 |     },
721 |     {
722 |      "data": {
723 |       "image/png": 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\n",
724 |       "text/plain": [
725 |        "<Figure size 432x288 with 2 Axes>"
726 |       ]
727 |      },
728 |      "metadata": {
729 |       "needs_background": "light"
730 |      },
731 |      "output_type": "display_data"
732 |     }
733 |    ],
734 |    "source": [
735 |     "plot_confusion_matrix(dtModel,features_test,ifMalware_test)"
736 |    ]
737 |   },
738 |   {
739 |    "cell_type": "code",
740 |    "execution_count": 19,
741 |    "id": "caaf81cf",
742 |    "metadata": {},
743 |    "outputs": [
744 |     {
745 |      "data": {
746 |       "text/plain": [
747 |        "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x121e7ab20>"
748 |       ]
749 |      },
750 |      "execution_count": 19,
751 |      "metadata": {},
752 |      "output_type": "execute_result"
753 |     },
754 |     {
755 |      "data": {
756 |       "image/png": 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\n",
757 |       "text/plain": [
758 |        "<Figure size 432x288 with 1 Axes>"
759 |       ]
760 |      },
761 |      "metadata": {
762 |       "needs_background": "light"
763 |      },
764 |      "output_type": "display_data"
765 |     }
766 |    ],
767 |    "source": [
768 |     "plot_precision_recall_curve(dtModel,features_test,ifMalware_test)"
769 |    ]
770 |   },
771 |   {
772 |    "cell_type": "code",
773 |    "execution_count": 20,
774 |    "id": "a7d9cfc4",
775 |    "metadata": {},
776 |    "outputs": [
777 |     {
778 |      "data": {
779 |       "text/plain": [
780 |        "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x123144550>"
781 |       ]
782 |      },
783 |      "execution_count": 20,
784 |      "metadata": {},
785 |      "output_type": "execute_result"
786 |     },
787 |     {
788 |      "data": {
789 |       "image/png": 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\n",
790 |       "text/plain": [
791 |        "<Figure size 432x288 with 1 Axes>"
792 |       ]
793 |      },
794 |      "metadata": {
795 |       "needs_background": "light"
796 |      },
797 |      "output_type": "display_data"
798 |     }
799 |    ],
800 |    "source": [
801 |     "plot_roc_curve(dtModel,features_test,ifMalware_test)"
802 |    ]
803 |   },
804 |   {
805 |    "cell_type": "code",
806 |    "execution_count": null,
807 |    "id": "7cc50825",
808 |    "metadata": {},
809 |    "outputs": [],
810 |    "source": []
811 |   }
812 |  ],
813 |  "metadata": {
814 |   "kernelspec": {
815 |    "display_name": "Python 3 (ipykernel)",
816 |    "language": "python",
817 |    "name": "python3"
818 |   },
819 |   "language_info": {
820 |    "codemirror_mode": {
821 |     "name": "ipython",
822 |     "version": 3
823 |    },
824 |    "file_extension": ".py",
825 |    "mimetype": "text/x-python",
826 |    "name": "python",
827 |    "nbconvert_exporter": "python",
828 |    "pygments_lexer": "ipython3",
829 |    "version": "3.9.10"
830 |   }
831 |  },
832 |  "nbformat": 4,
833 |  "nbformat_minor": 5
834 | }
835 | 


--------------------------------------------------------------------------------
/K_Nearest_Neighbors.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "75878b0c",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import pandas as pd\n",
 12 |     "from sklearn.model_selection import train_test_split\n",
 13 |     "from sklearn.neighbors import KNeighborsClassifier\n",
 14 |     "from sklearn.metrics import f1_score\n",
 15 |     "from sklearn.metrics import plot_confusion_matrix,plot_precision_recall_curve,plot_roc_curve"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "markdown",
 20 |    "id": "69b25522",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "Data set reading :"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 2,
 29 |    "id": "b54e1b40",
 30 |    "metadata": {},
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "data = pd.read_csv('../Malware-Detection-Using-Machine-Learning/data-set/MalwareDataSet.csv') "
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": 3,
 39 |    "id": "47c9ba1b",
 40 |    "metadata": {},
 41 |    "outputs": [
 42 |     {
 43 |      "data": {
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207 |        "  </tbody>\n",
208 |        "</table>\n",
209 |        "<p>137444 rows × 9 columns</p>\n",
210 |        "</div>"
211 |       ],
212 |       "text/plain": [
213 |        "        AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
214 |        "0                     10407                   9                  6   \n",
215 |        "1                      5354                   9                  6   \n",
216 |        "2                     58807                   9                  6   \n",
217 |        "3                     25166                   9                  6   \n",
218 |        "4                     70387                   9                  6   \n",
219 |        "...                     ...                 ...                ...   \n",
220 |        "137439               123291                  11                  0   \n",
221 |        "137440                40000                   2                  6   \n",
222 |        "137441                59610                  10                  0   \n",
223 |        "137442                51216                   2                  0   \n",
224 |        "137443                22731                  11                  0   \n",
225 |        "\n",
226 |        "        MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
227 |        "0                                 6               33088              262144   \n",
228 |        "1                                 6               33088              262144   \n",
229 |        "2                                 6               33088              262144   \n",
230 |        "3                                 6               33088              262144   \n",
231 |        "4                                 6               33088              262144   \n",
232 |        "...                             ...                 ...                 ...   \n",
233 |        "137439                            5               33088             1048576   \n",
234 |        "137440                            1               32768             1048576   \n",
235 |        "137441                            5               33088             1048576   \n",
236 |        "137442                            1                   0             1048576   \n",
237 |        "137443                            5               33088             1048576   \n",
238 |        "\n",
239 |        "        NumberOfSections  ResourceSize  legitimate  \n",
240 |        "0                      4           952           1  \n",
241 |        "1                      4           952           1  \n",
242 |        "2                      4        136490           1  \n",
243 |        "3                      4          1940           1  \n",
244 |        "4                      4         83098           1  \n",
245 |        "...                  ...           ...         ...  \n",
246 |        "137439                 5         81654           0  \n",
247 |        "137440                 8         67624           0  \n",
248 |        "137441                 5         22648           0  \n",
249 |        "137442                 8          2216           0  \n",
250 |        "137443                 5        318464           0  \n",
251 |        "\n",
252 |        "[137444 rows x 9 columns]"
253 |       ]
254 |      },
255 |      "execution_count": 3,
256 |      "metadata": {},
257 |      "output_type": "execute_result"
258 |     }
259 |    ],
260 |    "source": [
261 |     "data"
262 |    ]
263 |   },
264 |   {
265 |    "cell_type": "code",
266 |    "execution_count": 4,
267 |    "id": "b13348ab",
268 |    "metadata": {},
269 |    "outputs": [
270 |     {
271 |      "data": {
272 |       "text/plain": [
273 |        "(137444, 9)"
274 |       ]
275 |      },
276 |      "execution_count": 4,
277 |      "metadata": {},
278 |      "output_type": "execute_result"
279 |     }
280 |    ],
281 |    "source": [
282 |     "data.shape"
283 |    ]
284 |   },
285 |   {
286 |    "cell_type": "code",
287 |    "execution_count": 5,
288 |    "id": "33292bb5",
289 |    "metadata": {},
290 |    "outputs": [
291 |     {
292 |      "data": {
293 |       "text/html": [
294 |        "<div>\n",
295 |        "<style scoped>\n",
296 |        "    .dataframe tbody tr th:only-of-type {\n",
297 |        "        vertical-align: middle;\n",
298 |        "    }\n",
299 |        "\n",
300 |        "    .dataframe tbody tr th {\n",
301 |        "        vertical-align: top;\n",
302 |        "    }\n",
303 |        "\n",
304 |        "    .dataframe thead th {\n",
305 |        "        text-align: right;\n",
306 |        "    }\n",
307 |        "</style>\n",
308 |        "<table border=\"1\" class=\"dataframe\">\n",
309 |        "  <thead>\n",
310 |        "    <tr style=\"text-align: right;\">\n",
311 |        "      <th></th>\n",
312 |        "      <th>AddressOfEntryPoint</th>\n",
313 |        "      <th>MajorLinkerVersion</th>\n",
314 |        "      <th>MajorImageVersion</th>\n",
315 |        "      <th>MajorOperatingSystemVersion</th>\n",
316 |        "      <th>DllCharacteristics</th>\n",
317 |        "      <th>SizeOfStackReserve</th>\n",
318 |        "      <th>NumberOfSections</th>\n",
319 |        "      <th>ResourceSize</th>\n",
320 |        "      <th>legitimate</th>\n",
321 |        "    </tr>\n",
322 |        "  </thead>\n",
323 |        "  <tbody>\n",
324 |        "    <tr>\n",
325 |        "      <th>count</th>\n",
326 |        "      <td>1.374440e+05</td>\n",
327 |        "      <td>137444.000000</td>\n",
328 |        "      <td>137444.000000</td>\n",
329 |        "      <td>137444.000000</td>\n",
330 |        "      <td>137444.000000</td>\n",
331 |        "      <td>1.374440e+05</td>\n",
332 |        "      <td>137444.000000</td>\n",
333 |        "      <td>1.374440e+05</td>\n",
334 |        "      <td>137444.000000</td>\n",
335 |        "    </tr>\n",
336 |        "    <tr>\n",
337 |        "      <th>mean</th>\n",
338 |        "      <td>1.722186e+05</td>\n",
339 |        "      <td>8.620784</td>\n",
340 |        "      <td>68.731876</td>\n",
341 |        "      <td>5.098738</td>\n",
342 |        "      <td>22301.043436</td>\n",
343 |        "      <td>9.306841e+05</td>\n",
344 |        "      <td>4.997119</td>\n",
345 |        "      <td>2.474766e+05</td>\n",
346 |        "      <td>0.297707</td>\n",
347 |        "    </tr>\n",
348 |        "    <tr>\n",
349 |        "      <th>std</th>\n",
350 |        "      <td>3.438014e+06</td>\n",
351 |        "      <td>4.095635</td>\n",
352 |        "      <td>1185.709873</td>\n",
353 |        "      <td>99.437584</td>\n",
354 |        "      <td>15444.753219</td>\n",
355 |        "      <td>5.553175e+05</td>\n",
356 |        "      <td>1.917237</td>\n",
357 |        "      <td>2.129516e+07</td>\n",
358 |        "      <td>0.457252</td>\n",
359 |        "    </tr>\n",
360 |        "    <tr>\n",
361 |        "      <th>min</th>\n",
362 |        "      <td>0.000000e+00</td>\n",
363 |        "      <td>0.000000</td>\n",
364 |        "      <td>0.000000</td>\n",
365 |        "      <td>0.000000</td>\n",
366 |        "      <td>0.000000</td>\n",
367 |        "      <td>0.000000e+00</td>\n",
368 |        "      <td>1.000000</td>\n",
369 |        "      <td>0.000000e+00</td>\n",
370 |        "      <td>0.000000</td>\n",
371 |        "    </tr>\n",
372 |        "    <tr>\n",
373 |        "      <th>25%</th>\n",
374 |        "      <td>1.272100e+04</td>\n",
375 |        "      <td>8.000000</td>\n",
376 |        "      <td>0.000000</td>\n",
377 |        "      <td>4.000000</td>\n",
378 |        "      <td>320.000000</td>\n",
379 |        "      <td>1.048576e+06</td>\n",
380 |        "      <td>4.000000</td>\n",
381 |        "      <td>2.216000e+03</td>\n",
382 |        "      <td>0.000000</td>\n",
383 |        "    </tr>\n",
384 |        "    <tr>\n",
385 |        "      <th>50%</th>\n",
386 |        "      <td>5.300800e+04</td>\n",
387 |        "      <td>9.000000</td>\n",
388 |        "      <td>0.000000</td>\n",
389 |        "      <td>5.000000</td>\n",
390 |        "      <td>33088.000000</td>\n",
391 |        "      <td>1.048576e+06</td>\n",
392 |        "      <td>5.000000</td>\n",
393 |        "      <td>9.640000e+03</td>\n",
394 |        "      <td>0.000000</td>\n",
395 |        "    </tr>\n",
396 |        "    <tr>\n",
397 |        "      <th>75%</th>\n",
398 |        "      <td>6.157800e+04</td>\n",
399 |        "      <td>10.000000</td>\n",
400 |        "      <td>6.000000</td>\n",
401 |        "      <td>5.000000</td>\n",
402 |        "      <td>33088.000000</td>\n",
403 |        "      <td>1.048576e+06</td>\n",
404 |        "      <td>5.000000</td>\n",
405 |        "      <td>2.376250e+04</td>\n",
406 |        "      <td>1.000000</td>\n",
407 |        "    </tr>\n",
408 |        "    <tr>\n",
409 |        "      <th>max</th>\n",
410 |        "      <td>1.074484e+09</td>\n",
411 |        "      <td>255.000000</td>\n",
412 |        "      <td>28619.000000</td>\n",
413 |        "      <td>36868.000000</td>\n",
414 |        "      <td>49504.000000</td>\n",
415 |        "      <td>3.355443e+07</td>\n",
416 |        "      <td>40.000000</td>\n",
417 |        "      <td>4.294903e+09</td>\n",
418 |        "      <td>1.000000</td>\n",
419 |        "    </tr>\n",
420 |        "  </tbody>\n",
421 |        "</table>\n",
422 |        "</div>"
423 |       ],
424 |       "text/plain": [
425 |        "       AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
426 |        "count         1.374440e+05       137444.000000      137444.000000   \n",
427 |        "mean          1.722186e+05            8.620784          68.731876   \n",
428 |        "std           3.438014e+06            4.095635        1185.709873   \n",
429 |        "min           0.000000e+00            0.000000           0.000000   \n",
430 |        "25%           1.272100e+04            8.000000           0.000000   \n",
431 |        "50%           5.300800e+04            9.000000           0.000000   \n",
432 |        "75%           6.157800e+04           10.000000           6.000000   \n",
433 |        "max           1.074484e+09          255.000000       28619.000000   \n",
434 |        "\n",
435 |        "       MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
436 |        "count                137444.000000       137444.000000        1.374440e+05   \n",
437 |        "mean                      5.098738        22301.043436        9.306841e+05   \n",
438 |        "std                      99.437584        15444.753219        5.553175e+05   \n",
439 |        "min                       0.000000            0.000000        0.000000e+00   \n",
440 |        "25%                       4.000000          320.000000        1.048576e+06   \n",
441 |        "50%                       5.000000        33088.000000        1.048576e+06   \n",
442 |        "75%                       5.000000        33088.000000        1.048576e+06   \n",
443 |        "max                   36868.000000        49504.000000        3.355443e+07   \n",
444 |        "\n",
445 |        "       NumberOfSections  ResourceSize     legitimate  \n",
446 |        "count     137444.000000  1.374440e+05  137444.000000  \n",
447 |        "mean           4.997119  2.474766e+05       0.297707  \n",
448 |        "std            1.917237  2.129516e+07       0.457252  \n",
449 |        "min            1.000000  0.000000e+00       0.000000  \n",
450 |        "25%            4.000000  2.216000e+03       0.000000  \n",
451 |        "50%            5.000000  9.640000e+03       0.000000  \n",
452 |        "75%            5.000000  2.376250e+04       1.000000  \n",
453 |        "max           40.000000  4.294903e+09       1.000000  "
454 |       ]
455 |      },
456 |      "execution_count": 5,
457 |      "metadata": {},
458 |      "output_type": "execute_result"
459 |     }
460 |    ],
461 |    "source": [
462 |     "data.describe()"
463 |    ]
464 |   },
465 |   {
466 |    "cell_type": "code",
467 |    "execution_count": 6,
468 |    "id": "65dbf455",
469 |    "metadata": {
470 |     "scrolled": true
471 |    },
472 |    "outputs": [
473 |     {
474 |      "data": {
475 |       "text/plain": [
476 |        "legitimate\n",
477 |        "0    96526\n",
478 |        "1    40918\n",
479 |        "dtype: int64"
480 |       ]
481 |      },
482 |      "execution_count": 6,
483 |      "metadata": {},
484 |      "output_type": "execute_result"
485 |     }
486 |    ],
487 |    "source": [
488 |     "data.groupby(data['legitimate']).size()"
489 |    ]
490 |   },
491 |   {
492 |    "cell_type": "markdown",
493 |    "id": "2eab99e9",
494 |    "metadata": {},
495 |    "source": [
496 |     "We have a total of 137.444 data. There are 96.526 safe and 40.918 malware."
497 |    ]
498 |   },
499 |   {
500 |    "cell_type": "code",
501 |    "execution_count": 7,
502 |    "id": "8592b076",
503 |    "metadata": {},
504 |    "outputs": [],
505 |    "source": [
506 |     "features = data.iloc[:,[0,1,2,3,4,5,6,7]].values # extracting the first 8 columns from the dataset - features"
507 |    ]
508 |   },
509 |   {
510 |    "cell_type": "code",
511 |    "execution_count": 8,
512 |    "id": "3da1b6cd",
513 |    "metadata": {},
514 |    "outputs": [
515 |     {
516 |      "data": {
517 |       "text/plain": [
518 |        "array([[  10407,       9,       6, ...,  262144,       4,     952],\n",
519 |        "       [   5354,       9,       6, ...,  262144,       4,     952],\n",
520 |        "       [  58807,       9,       6, ...,  262144,       4,  136490],\n",
521 |        "       ...,\n",
522 |        "       [  59610,      10,       0, ..., 1048576,       5,   22648],\n",
523 |        "       [  51216,       2,       0, ..., 1048576,       8,    2216],\n",
524 |        "       [  22731,      11,       0, ..., 1048576,       5,  318464]])"
525 |       ]
526 |      },
527 |      "execution_count": 8,
528 |      "metadata": {},
529 |      "output_type": "execute_result"
530 |     }
531 |    ],
532 |    "source": [
533 |     "features"
534 |    ]
535 |   },
536 |   {
537 |    "cell_type": "code",
538 |    "execution_count": 9,
539 |    "id": "46db9f27",
540 |    "metadata": {},
541 |    "outputs": [],
542 |    "source": [
543 |     "ifMalware = data.iloc[:,8].values  # extracting the legitimate column of the dataset - safe & malware results"
544 |    ]
545 |   },
546 |   {
547 |    "cell_type": "code",
548 |    "execution_count": 10,
549 |    "id": "c0e0d3db",
550 |    "metadata": {},
551 |    "outputs": [
552 |     {
553 |      "data": {
554 |       "text/plain": [
555 |        "array([1, 1, 1, ..., 0, 0, 0])"
556 |       ]
557 |      },
558 |      "execution_count": 10,
559 |      "metadata": {},
560 |      "output_type": "execute_result"
561 |     }
562 |    ],
563 |    "source": [
564 |     "ifMalware"
565 |    ]
566 |   },
567 |   {
568 |    "cell_type": "markdown",
569 |    "id": "773b9a86",
570 |    "metadata": {},
571 |    "source": [
572 |     "We will use 75% of our dataset for training and 25% for testing :"
573 |    ]
574 |   },
575 |   {
576 |    "cell_type": "code",
577 |    "execution_count": 11,
578 |    "id": "0657650f",
579 |    "metadata": {},
580 |    "outputs": [],
581 |    "source": [
582 |     "features_train, features_test, ifMalware_train, ifMalware_test = train_test_split(features, ifMalware, test_size=0.25)"
583 |    ]
584 |   },
585 |   {
586 |    "cell_type": "markdown",
587 |    "id": "4edcf3e5",
588 |    "metadata": {},
589 |    "source": [
590 |     "Learning and Predict :"
591 |    ]
592 |   },
593 |   {
594 |    "cell_type": "code",
595 |    "execution_count": 12,
596 |    "id": "30274115",
597 |    "metadata": {},
598 |    "outputs": [],
599 |    "source": [
600 |     "knModel = KNeighborsClassifier(n_neighbors=1) # Defined the model."
601 |    ]
602 |   },
603 |   {
604 |    "cell_type": "code",
605 |    "execution_count": 13,
606 |    "id": "b6993294",
607 |    "metadata": {},
608 |    "outputs": [
609 |     {
610 |      "data": {
611 |       "text/plain": [
612 |        "KNeighborsClassifier(n_neighbors=1)"
613 |       ]
614 |      },
615 |      "execution_count": 13,
616 |      "metadata": {},
617 |      "output_type": "execute_result"
618 |     }
619 |    ],
620 |    "source": [
621 |     "knModel.fit(features_train, ifMalware_train) # Provided training data."
622 |    ]
623 |   },
624 |   {
625 |    "cell_type": "code",
626 |    "execution_count": 14,
627 |    "id": "0a413829",
628 |    "metadata": {},
629 |    "outputs": [],
630 |    "source": [
631 |     "knPredict = knModel.predict(features_test) # Give the test data then call predict."
632 |    ]
633 |   },
634 |   {
635 |    "cell_type": "markdown",
636 |    "id": "9d67adc3",
637 |    "metadata": {},
638 |    "source": [
639 |     "Results :"
640 |    ]
641 |   },
642 |   {
643 |    "cell_type": "code",
644 |    "execution_count": 15,
645 |    "id": "42f2a30d",
646 |    "metadata": {},
647 |    "outputs": [
648 |     {
649 |      "name": "stdout",
650 |      "output_type": "stream",
651 |      "text": [
652 |       "Number of mislabeled out of a total of 34361 test entries: 838\n"
653 |      ]
654 |     }
655 |    ],
656 |    "source": [
657 |     "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
658 |     "                                                                      (ifMalware_test != knPredict).sum()))"
659 |    ]
660 |   },
661 |   {
662 |    "cell_type": "code",
663 |    "execution_count": 16,
664 |    "id": "cbbfc4fb",
665 |    "metadata": {},
666 |    "outputs": [],
667 |    "source": [
668 |     "successRate = 100 * f1_score(ifMalware_test, knPredict, average='micro') # Success rate calculation"
669 |    ]
670 |   },
671 |   {
672 |    "cell_type": "code",
673 |    "execution_count": 17,
674 |    "id": "894b2b35",
675 |    "metadata": {},
676 |    "outputs": [
677 |     {
678 |      "name": "stdout",
679 |      "output_type": "stream",
680 |      "text": [
681 |       "The Success Rate was calculated as % : 97.56118855679404 with the K-Nearest-Neighbors\n"
682 |      ]
683 |     }
684 |    ],
685 |    "source": [
686 |     "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with the K-Nearest-Neighbors\")"
687 |    ]
688 |   },
689 |   {
690 |    "cell_type": "markdown",
691 |    "id": "ffda807e",
692 |    "metadata": {},
693 |    "source": [
694 |     "Visualization :"
695 |    ]
696 |   },
697 |   {
698 |    "cell_type": "code",
699 |    "execution_count": 18,
700 |    "id": "a1af503a",
701 |    "metadata": {},
702 |    "outputs": [
703 |     {
704 |      "data": {
705 |       "text/plain": [
706 |        "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x123674df0>"
707 |       ]
708 |      },
709 |      "execution_count": 18,
710 |      "metadata": {},
711 |      "output_type": "execute_result"
712 |     },
713 |     {
714 |      "data": {
715 |       "image/png": 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\n",
716 |       "text/plain": [
717 |        "<Figure size 432x288 with 2 Axes>"
718 |       ]
719 |      },
720 |      "metadata": {
721 |       "needs_background": "light"
722 |      },
723 |      "output_type": "display_data"
724 |     }
725 |    ],
726 |    "source": [
727 |     "plot_confusion_matrix(knModel,features_test,ifMalware_test)"
728 |    ]
729 |   },
730 |   {
731 |    "cell_type": "code",
732 |    "execution_count": 19,
733 |    "id": "396f5630",
734 |    "metadata": {},
735 |    "outputs": [
736 |     {
737 |      "data": {
738 |       "text/plain": [
739 |        "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x123f4cd90>"
740 |       ]
741 |      },
742 |      "execution_count": 19,
743 |      "metadata": {},
744 |      "output_type": "execute_result"
745 |     },
746 |     {
747 |      "data": {
748 |       "image/png": 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\n",
749 |       "text/plain": [
750 |        "<Figure size 432x288 with 1 Axes>"
751 |       ]
752 |      },
753 |      "metadata": {
754 |       "needs_background": "light"
755 |      },
756 |      "output_type": "display_data"
757 |     }
758 |    ],
759 |    "source": [
760 |     "plot_precision_recall_curve(knModel,features_test,ifMalware_test)"
761 |    ]
762 |   },
763 |   {
764 |    "cell_type": "code",
765 |    "execution_count": 20,
766 |    "id": "6aced8b7",
767 |    "metadata": {},
768 |    "outputs": [
769 |     {
770 |      "data": {
771 |       "text/plain": [
772 |        "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x12416bbe0>"
773 |       ]
774 |      },
775 |      "execution_count": 20,
776 |      "metadata": {},
777 |      "output_type": "execute_result"
778 |     },
779 |     {
780 |      "data": {
781 |       "image/png": 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\n",
782 |       "text/plain": [
783 |        "<Figure size 432x288 with 1 Axes>"
784 |       ]
785 |      },
786 |      "metadata": {
787 |       "needs_background": "light"
788 |      },
789 |      "output_type": "display_data"
790 |     }
791 |    ],
792 |    "source": [
793 |     "plot_roc_curve(knModel,features_test,ifMalware_test)"
794 |    ]
795 |   },
796 |   {
797 |    "cell_type": "code",
798 |    "execution_count": null,
799 |    "id": "038c4e87",
800 |    "metadata": {},
801 |    "outputs": [],
802 |    "source": []
803 |   }
804 |  ],
805 |  "metadata": {
806 |   "kernelspec": {
807 |    "display_name": "Python 3 (ipykernel)",
808 |    "language": "python",
809 |    "name": "python3"
810 |   },
811 |   "language_info": {
812 |    "codemirror_mode": {
813 |     "name": "ipython",
814 |     "version": 3
815 |    },
816 |    "file_extension": ".py",
817 |    "mimetype": "text/x-python",
818 |    "name": "python",
819 |    "nbconvert_exporter": "python",
820 |    "pygments_lexer": "ipython3",
821 |    "version": "3.9.10"
822 |   }
823 |  },
824 |  "nbformat": 4,
825 |  "nbformat_minor": 5
826 | }
827 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2022 Emrah Yıldırım
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Malware-Detection-Using-Machine-Learning
 2 | 
 3 | <!-- ABOUT THE PROJECT -->
 4 | ## About The Project
 5 | 
 6 | * This project analyzes PE information of exe files to detect malware.  
 7 | * In this repository you will learn how to create your own dataset and will be able to see the use of machine learning models using the dataset.  
 8 | * We will use machine learning for detect malware.
 9 | 
10 | 
11 | ## Getting Started
12 | This pe information was extracted using pefile.
13 | 
14 | If you want to create your own dataset, check out ```Data_Set_Generator.ipynb```
15 | 
16 | or you can use ```data-set/MalwareDataSet.csv```
17 | 
18 | ## Dataset 
19 | 
20 | This dataset (MalwareDataSet.csv) contains a total of ```137,444 data```. 
21 | There are ```96,526 safe and 40,918 malware```.
22 | 
23 | It has a total of 9 columns. The information of these columns is as follows:
24 | ```
25 | * AddressOfEntryPoint
26 | * MajorLinkerVersion
27 | * MajorImageVersion
28 | * MajorOperatingSystemVersion
29 | * DllCharacteristics
30 | * SizeOfStackReserve
31 | * NumberOfSections
32 | * ResourceSize
33 | * legitimate
34 | ```
35 | 
36 | 
37 | ## Classification
38 | 
39 | We used 3 different classification algorithms. These :
40 | ```
41 | * K Nearest Neighbors (KNN)
42 | * Decision Tree
43 | * Random Forest
44 | ```
45 | 
46 | 
47 | ## Results
48 | ```
49 | The Success Rate was calculated as % 97.56118855679404 with the K-Nearest-Neighbors
50 | 
51 | The Success Rate was calculated as % 98.59433660254359 with the Decision Tree
52 | 
53 | The Success Rate was calculated as % 99.1240068682518 with the Random Forest
54 | ```
55 | 
56 | 
57 | 
58 | 
59 | 


--------------------------------------------------------------------------------
/Random_Forest.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "918bf7bd",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Malware Analysis With Machine Learning - Random Forest"
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": 1,
 14 |    "id": "034fde8d",
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import numpy as np\n",
 19 |     "import pandas as pd\n",
 20 |     "from sklearn.model_selection import train_test_split\n",
 21 |     "from sklearn.ensemble import RandomForestClassifier\n",
 22 |     "from sklearn.metrics import f1_score\n",
 23 |     "from sklearn.metrics import plot_confusion_matrix,plot_precision_recall_curve,plot_roc_curve"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "markdown",
 28 |    "id": "5c6b661b",
 29 |    "metadata": {},
 30 |    "source": [
 31 |     "Data set reading :"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 2,
 37 |    "id": "c4e18ea4",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "data = pd.read_csv('../Malware-Detection-Using-Machine-Learning/data-set/MalwareDataSet.csv') "
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 3,
 47 |    "id": "386e250f",
 48 |    "metadata": {},
 49 |    "outputs": [
 50 |     {
 51 |      "data": {
 52 |       "text/html": [
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 54 |        "<style scoped>\n",
 55 |        "    .dataframe tbody tr th:only-of-type {\n",
 56 |        "        vertical-align: middle;\n",
 57 |        "    }\n",
 58 |        "\n",
 59 |        "    .dataframe tbody tr th {\n",
 60 |        "        vertical-align: top;\n",
 61 |        "    }\n",
 62 |        "\n",
 63 |        "    .dataframe thead th {\n",
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 65 |        "    }\n",
 66 |        "</style>\n",
 67 |        "<table border=\"1\" class=\"dataframe\">\n",
 68 |        "  <thead>\n",
 69 |        "    <tr style=\"text-align: right;\">\n",
 70 |        "      <th></th>\n",
 71 |        "      <th>AddressOfEntryPoint</th>\n",
 72 |        "      <th>MajorLinkerVersion</th>\n",
 73 |        "      <th>MajorImageVersion</th>\n",
 74 |        "      <th>MajorOperatingSystemVersion</th>\n",
 75 |        "      <th>DllCharacteristics</th>\n",
 76 |        "      <th>SizeOfStackReserve</th>\n",
 77 |        "      <th>NumberOfSections</th>\n",
 78 |        "      <th>ResourceSize</th>\n",
 79 |        "      <th>legitimate</th>\n",
 80 |        "    </tr>\n",
 81 |        "  </thead>\n",
 82 |        "  <tbody>\n",
 83 |        "    <tr>\n",
 84 |        "      <th>0</th>\n",
 85 |        "      <td>10407</td>\n",
 86 |        "      <td>9</td>\n",
 87 |        "      <td>6</td>\n",
 88 |        "      <td>6</td>\n",
 89 |        "      <td>33088</td>\n",
 90 |        "      <td>262144</td>\n",
 91 |        "      <td>4</td>\n",
 92 |        "      <td>952</td>\n",
 93 |        "      <td>1</td>\n",
 94 |        "    </tr>\n",
 95 |        "    <tr>\n",
 96 |        "      <th>1</th>\n",
 97 |        "      <td>5354</td>\n",
 98 |        "      <td>9</td>\n",
 99 |        "      <td>6</td>\n",
100 |        "      <td>6</td>\n",
101 |        "      <td>33088</td>\n",
102 |        "      <td>262144</td>\n",
103 |        "      <td>4</td>\n",
104 |        "      <td>952</td>\n",
105 |        "      <td>1</td>\n",
106 |        "    </tr>\n",
107 |        "    <tr>\n",
108 |        "      <th>2</th>\n",
109 |        "      <td>58807</td>\n",
110 |        "      <td>9</td>\n",
111 |        "      <td>6</td>\n",
112 |        "      <td>6</td>\n",
113 |        "      <td>33088</td>\n",
114 |        "      <td>262144</td>\n",
115 |        "      <td>4</td>\n",
116 |        "      <td>136490</td>\n",
117 |        "      <td>1</td>\n",
118 |        "    </tr>\n",
119 |        "    <tr>\n",
120 |        "      <th>3</th>\n",
121 |        "      <td>25166</td>\n",
122 |        "      <td>9</td>\n",
123 |        "      <td>6</td>\n",
124 |        "      <td>6</td>\n",
125 |        "      <td>33088</td>\n",
126 |        "      <td>262144</td>\n",
127 |        "      <td>4</td>\n",
128 |        "      <td>1940</td>\n",
129 |        "      <td>1</td>\n",
130 |        "    </tr>\n",
131 |        "    <tr>\n",
132 |        "      <th>4</th>\n",
133 |        "      <td>70387</td>\n",
134 |        "      <td>9</td>\n",
135 |        "      <td>6</td>\n",
136 |        "      <td>6</td>\n",
137 |        "      <td>33088</td>\n",
138 |        "      <td>262144</td>\n",
139 |        "      <td>4</td>\n",
140 |        "      <td>83098</td>\n",
141 |        "      <td>1</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>...</th>\n",
145 |        "      <td>...</td>\n",
146 |        "      <td>...</td>\n",
147 |        "      <td>...</td>\n",
148 |        "      <td>...</td>\n",
149 |        "      <td>...</td>\n",
150 |        "      <td>...</td>\n",
151 |        "      <td>...</td>\n",
152 |        "      <td>...</td>\n",
153 |        "      <td>...</td>\n",
154 |        "    </tr>\n",
155 |        "    <tr>\n",
156 |        "      <th>137439</th>\n",
157 |        "      <td>123291</td>\n",
158 |        "      <td>11</td>\n",
159 |        "      <td>0</td>\n",
160 |        "      <td>5</td>\n",
161 |        "      <td>33088</td>\n",
162 |        "      <td>1048576</td>\n",
163 |        "      <td>5</td>\n",
164 |        "      <td>81654</td>\n",
165 |        "      <td>0</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>137440</th>\n",
169 |        "      <td>40000</td>\n",
170 |        "      <td>2</td>\n",
171 |        "      <td>6</td>\n",
172 |        "      <td>1</td>\n",
173 |        "      <td>32768</td>\n",
174 |        "      <td>1048576</td>\n",
175 |        "      <td>8</td>\n",
176 |        "      <td>67624</td>\n",
177 |        "      <td>0</td>\n",
178 |        "    </tr>\n",
179 |        "    <tr>\n",
180 |        "      <th>137441</th>\n",
181 |        "      <td>59610</td>\n",
182 |        "      <td>10</td>\n",
183 |        "      <td>0</td>\n",
184 |        "      <td>5</td>\n",
185 |        "      <td>33088</td>\n",
186 |        "      <td>1048576</td>\n",
187 |        "      <td>5</td>\n",
188 |        "      <td>22648</td>\n",
189 |        "      <td>0</td>\n",
190 |        "    </tr>\n",
191 |        "    <tr>\n",
192 |        "      <th>137442</th>\n",
193 |        "      <td>51216</td>\n",
194 |        "      <td>2</td>\n",
195 |        "      <td>0</td>\n",
196 |        "      <td>1</td>\n",
197 |        "      <td>0</td>\n",
198 |        "      <td>1048576</td>\n",
199 |        "      <td>8</td>\n",
200 |        "      <td>2216</td>\n",
201 |        "      <td>0</td>\n",
202 |        "    </tr>\n",
203 |        "    <tr>\n",
204 |        "      <th>137443</th>\n",
205 |        "      <td>22731</td>\n",
206 |        "      <td>11</td>\n",
207 |        "      <td>0</td>\n",
208 |        "      <td>5</td>\n",
209 |        "      <td>33088</td>\n",
210 |        "      <td>1048576</td>\n",
211 |        "      <td>5</td>\n",
212 |        "      <td>318464</td>\n",
213 |        "      <td>0</td>\n",
214 |        "    </tr>\n",
215 |        "  </tbody>\n",
216 |        "</table>\n",
217 |        "<p>137444 rows × 9 columns</p>\n",
218 |        "</div>"
219 |       ],
220 |       "text/plain": [
221 |        "        AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
222 |        "0                     10407                   9                  6   \n",
223 |        "1                      5354                   9                  6   \n",
224 |        "2                     58807                   9                  6   \n",
225 |        "3                     25166                   9                  6   \n",
226 |        "4                     70387                   9                  6   \n",
227 |        "...                     ...                 ...                ...   \n",
228 |        "137439               123291                  11                  0   \n",
229 |        "137440                40000                   2                  6   \n",
230 |        "137441                59610                  10                  0   \n",
231 |        "137442                51216                   2                  0   \n",
232 |        "137443                22731                  11                  0   \n",
233 |        "\n",
234 |        "        MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
235 |        "0                                 6               33088              262144   \n",
236 |        "1                                 6               33088              262144   \n",
237 |        "2                                 6               33088              262144   \n",
238 |        "3                                 6               33088              262144   \n",
239 |        "4                                 6               33088              262144   \n",
240 |        "...                             ...                 ...                 ...   \n",
241 |        "137439                            5               33088             1048576   \n",
242 |        "137440                            1               32768             1048576   \n",
243 |        "137441                            5               33088             1048576   \n",
244 |        "137442                            1                   0             1048576   \n",
245 |        "137443                            5               33088             1048576   \n",
246 |        "\n",
247 |        "        NumberOfSections  ResourceSize  legitimate  \n",
248 |        "0                      4           952           1  \n",
249 |        "1                      4           952           1  \n",
250 |        "2                      4        136490           1  \n",
251 |        "3                      4          1940           1  \n",
252 |        "4                      4         83098           1  \n",
253 |        "...                  ...           ...         ...  \n",
254 |        "137439                 5         81654           0  \n",
255 |        "137440                 8         67624           0  \n",
256 |        "137441                 5         22648           0  \n",
257 |        "137442                 8          2216           0  \n",
258 |        "137443                 5        318464           0  \n",
259 |        "\n",
260 |        "[137444 rows x 9 columns]"
261 |       ]
262 |      },
263 |      "execution_count": 3,
264 |      "metadata": {},
265 |      "output_type": "execute_result"
266 |     }
267 |    ],
268 |    "source": [
269 |     "data"
270 |    ]
271 |   },
272 |   {
273 |    "cell_type": "code",
274 |    "execution_count": 4,
275 |    "id": "dd389773",
276 |    "metadata": {},
277 |    "outputs": [
278 |     {
279 |      "data": {
280 |       "text/plain": [
281 |        "(137444, 9)"
282 |       ]
283 |      },
284 |      "execution_count": 4,
285 |      "metadata": {},
286 |      "output_type": "execute_result"
287 |     }
288 |    ],
289 |    "source": [
290 |     "data.shape"
291 |    ]
292 |   },
293 |   {
294 |    "cell_type": "code",
295 |    "execution_count": 5,
296 |    "id": "5c146794",
297 |    "metadata": {},
298 |    "outputs": [
299 |     {
300 |      "data": {
301 |       "text/html": [
302 |        "<div>\n",
303 |        "<style scoped>\n",
304 |        "    .dataframe tbody tr th:only-of-type {\n",
305 |        "        vertical-align: middle;\n",
306 |        "    }\n",
307 |        "\n",
308 |        "    .dataframe tbody tr th {\n",
309 |        "        vertical-align: top;\n",
310 |        "    }\n",
311 |        "\n",
312 |        "    .dataframe thead th {\n",
313 |        "        text-align: right;\n",
314 |        "    }\n",
315 |        "</style>\n",
316 |        "<table border=\"1\" class=\"dataframe\">\n",
317 |        "  <thead>\n",
318 |        "    <tr style=\"text-align: right;\">\n",
319 |        "      <th></th>\n",
320 |        "      <th>AddressOfEntryPoint</th>\n",
321 |        "      <th>MajorLinkerVersion</th>\n",
322 |        "      <th>MajorImageVersion</th>\n",
323 |        "      <th>MajorOperatingSystemVersion</th>\n",
324 |        "      <th>DllCharacteristics</th>\n",
325 |        "      <th>SizeOfStackReserve</th>\n",
326 |        "      <th>NumberOfSections</th>\n",
327 |        "      <th>ResourceSize</th>\n",
328 |        "      <th>legitimate</th>\n",
329 |        "    </tr>\n",
330 |        "  </thead>\n",
331 |        "  <tbody>\n",
332 |        "    <tr>\n",
333 |        "      <th>count</th>\n",
334 |        "      <td>1.374440e+05</td>\n",
335 |        "      <td>137444.000000</td>\n",
336 |        "      <td>137444.000000</td>\n",
337 |        "      <td>137444.000000</td>\n",
338 |        "      <td>137444.000000</td>\n",
339 |        "      <td>1.374440e+05</td>\n",
340 |        "      <td>137444.000000</td>\n",
341 |        "      <td>1.374440e+05</td>\n",
342 |        "      <td>137444.000000</td>\n",
343 |        "    </tr>\n",
344 |        "    <tr>\n",
345 |        "      <th>mean</th>\n",
346 |        "      <td>1.722186e+05</td>\n",
347 |        "      <td>8.620784</td>\n",
348 |        "      <td>68.731876</td>\n",
349 |        "      <td>5.098738</td>\n",
350 |        "      <td>22301.043436</td>\n",
351 |        "      <td>9.306841e+05</td>\n",
352 |        "      <td>4.997119</td>\n",
353 |        "      <td>2.474766e+05</td>\n",
354 |        "      <td>0.297707</td>\n",
355 |        "    </tr>\n",
356 |        "    <tr>\n",
357 |        "      <th>std</th>\n",
358 |        "      <td>3.438014e+06</td>\n",
359 |        "      <td>4.095635</td>\n",
360 |        "      <td>1185.709873</td>\n",
361 |        "      <td>99.437584</td>\n",
362 |        "      <td>15444.753219</td>\n",
363 |        "      <td>5.553175e+05</td>\n",
364 |        "      <td>1.917237</td>\n",
365 |        "      <td>2.129516e+07</td>\n",
366 |        "      <td>0.457252</td>\n",
367 |        "    </tr>\n",
368 |        "    <tr>\n",
369 |        "      <th>min</th>\n",
370 |        "      <td>0.000000e+00</td>\n",
371 |        "      <td>0.000000</td>\n",
372 |        "      <td>0.000000</td>\n",
373 |        "      <td>0.000000</td>\n",
374 |        "      <td>0.000000</td>\n",
375 |        "      <td>0.000000e+00</td>\n",
376 |        "      <td>1.000000</td>\n",
377 |        "      <td>0.000000e+00</td>\n",
378 |        "      <td>0.000000</td>\n",
379 |        "    </tr>\n",
380 |        "    <tr>\n",
381 |        "      <th>25%</th>\n",
382 |        "      <td>1.272100e+04</td>\n",
383 |        "      <td>8.000000</td>\n",
384 |        "      <td>0.000000</td>\n",
385 |        "      <td>4.000000</td>\n",
386 |        "      <td>320.000000</td>\n",
387 |        "      <td>1.048576e+06</td>\n",
388 |        "      <td>4.000000</td>\n",
389 |        "      <td>2.216000e+03</td>\n",
390 |        "      <td>0.000000</td>\n",
391 |        "    </tr>\n",
392 |        "    <tr>\n",
393 |        "      <th>50%</th>\n",
394 |        "      <td>5.300800e+04</td>\n",
395 |        "      <td>9.000000</td>\n",
396 |        "      <td>0.000000</td>\n",
397 |        "      <td>5.000000</td>\n",
398 |        "      <td>33088.000000</td>\n",
399 |        "      <td>1.048576e+06</td>\n",
400 |        "      <td>5.000000</td>\n",
401 |        "      <td>9.640000e+03</td>\n",
402 |        "      <td>0.000000</td>\n",
403 |        "    </tr>\n",
404 |        "    <tr>\n",
405 |        "      <th>75%</th>\n",
406 |        "      <td>6.157800e+04</td>\n",
407 |        "      <td>10.000000</td>\n",
408 |        "      <td>6.000000</td>\n",
409 |        "      <td>5.000000</td>\n",
410 |        "      <td>33088.000000</td>\n",
411 |        "      <td>1.048576e+06</td>\n",
412 |        "      <td>5.000000</td>\n",
413 |        "      <td>2.376250e+04</td>\n",
414 |        "      <td>1.000000</td>\n",
415 |        "    </tr>\n",
416 |        "    <tr>\n",
417 |        "      <th>max</th>\n",
418 |        "      <td>1.074484e+09</td>\n",
419 |        "      <td>255.000000</td>\n",
420 |        "      <td>28619.000000</td>\n",
421 |        "      <td>36868.000000</td>\n",
422 |        "      <td>49504.000000</td>\n",
423 |        "      <td>3.355443e+07</td>\n",
424 |        "      <td>40.000000</td>\n",
425 |        "      <td>4.294903e+09</td>\n",
426 |        "      <td>1.000000</td>\n",
427 |        "    </tr>\n",
428 |        "  </tbody>\n",
429 |        "</table>\n",
430 |        "</div>"
431 |       ],
432 |       "text/plain": [
433 |        "       AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
434 |        "count         1.374440e+05       137444.000000      137444.000000   \n",
435 |        "mean          1.722186e+05            8.620784          68.731876   \n",
436 |        "std           3.438014e+06            4.095635        1185.709873   \n",
437 |        "min           0.000000e+00            0.000000           0.000000   \n",
438 |        "25%           1.272100e+04            8.000000           0.000000   \n",
439 |        "50%           5.300800e+04            9.000000           0.000000   \n",
440 |        "75%           6.157800e+04           10.000000           6.000000   \n",
441 |        "max           1.074484e+09          255.000000       28619.000000   \n",
442 |        "\n",
443 |        "       MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
444 |        "count                137444.000000       137444.000000        1.374440e+05   \n",
445 |        "mean                      5.098738        22301.043436        9.306841e+05   \n",
446 |        "std                      99.437584        15444.753219        5.553175e+05   \n",
447 |        "min                       0.000000            0.000000        0.000000e+00   \n",
448 |        "25%                       4.000000          320.000000        1.048576e+06   \n",
449 |        "50%                       5.000000        33088.000000        1.048576e+06   \n",
450 |        "75%                       5.000000        33088.000000        1.048576e+06   \n",
451 |        "max                   36868.000000        49504.000000        3.355443e+07   \n",
452 |        "\n",
453 |        "       NumberOfSections  ResourceSize     legitimate  \n",
454 |        "count     137444.000000  1.374440e+05  137444.000000  \n",
455 |        "mean           4.997119  2.474766e+05       0.297707  \n",
456 |        "std            1.917237  2.129516e+07       0.457252  \n",
457 |        "min            1.000000  0.000000e+00       0.000000  \n",
458 |        "25%            4.000000  2.216000e+03       0.000000  \n",
459 |        "50%            5.000000  9.640000e+03       0.000000  \n",
460 |        "75%            5.000000  2.376250e+04       1.000000  \n",
461 |        "max           40.000000  4.294903e+09       1.000000  "
462 |       ]
463 |      },
464 |      "execution_count": 5,
465 |      "metadata": {},
466 |      "output_type": "execute_result"
467 |     }
468 |    ],
469 |    "source": [
470 |     "data.describe()"
471 |    ]
472 |   },
473 |   {
474 |    "cell_type": "code",
475 |    "execution_count": 6,
476 |    "id": "c4294347",
477 |    "metadata": {},
478 |    "outputs": [
479 |     {
480 |      "data": {
481 |       "text/plain": [
482 |        "legitimate\n",
483 |        "0    96526\n",
484 |        "1    40918\n",
485 |        "dtype: int64"
486 |       ]
487 |      },
488 |      "execution_count": 6,
489 |      "metadata": {},
490 |      "output_type": "execute_result"
491 |     }
492 |    ],
493 |    "source": [
494 |     "data.groupby(data['legitimate']).size()"
495 |    ]
496 |   },
497 |   {
498 |    "cell_type": "markdown",
499 |    "id": "2eab99e9",
500 |    "metadata": {},
501 |    "source": [
502 |     "We have a total of 137.444 data. There are 96.526 safe and 40.918 malware."
503 |    ]
504 |   },
505 |   {
506 |    "cell_type": "code",
507 |    "execution_count": 7,
508 |    "id": "b87ac400",
509 |    "metadata": {},
510 |    "outputs": [],
511 |    "source": [
512 |     "features = data.iloc[:,[0,1,2,3,4,5,6,7]].values # extracting the first 8 columns from the dataset - features"
513 |    ]
514 |   },
515 |   {
516 |    "cell_type": "code",
517 |    "execution_count": 8,
518 |    "id": "7dcb27ec",
519 |    "metadata": {},
520 |    "outputs": [
521 |     {
522 |      "data": {
523 |       "text/plain": [
524 |        "array([[  10407,       9,       6, ...,  262144,       4,     952],\n",
525 |        "       [   5354,       9,       6, ...,  262144,       4,     952],\n",
526 |        "       [  58807,       9,       6, ...,  262144,       4,  136490],\n",
527 |        "       ...,\n",
528 |        "       [  59610,      10,       0, ..., 1048576,       5,   22648],\n",
529 |        "       [  51216,       2,       0, ..., 1048576,       8,    2216],\n",
530 |        "       [  22731,      11,       0, ..., 1048576,       5,  318464]])"
531 |       ]
532 |      },
533 |      "execution_count": 8,
534 |      "metadata": {},
535 |      "output_type": "execute_result"
536 |     }
537 |    ],
538 |    "source": [
539 |     "features"
540 |    ]
541 |   },
542 |   {
543 |    "cell_type": "code",
544 |    "execution_count": 9,
545 |    "id": "137e5f7f",
546 |    "metadata": {},
547 |    "outputs": [],
548 |    "source": [
549 |     "ifMalware = data.iloc[:,8].values # extracting the legitimate column of the dataset - safe & malware results"
550 |    ]
551 |   },
552 |   {
553 |    "cell_type": "code",
554 |    "execution_count": 10,
555 |    "id": "52df4a9c",
556 |    "metadata": {},
557 |    "outputs": [
558 |     {
559 |      "data": {
560 |       "text/plain": [
561 |        "array([1, 1, 1, ..., 0, 0, 0])"
562 |       ]
563 |      },
564 |      "execution_count": 10,
565 |      "metadata": {},
566 |      "output_type": "execute_result"
567 |     }
568 |    ],
569 |    "source": [
570 |     "ifMalware"
571 |    ]
572 |   },
573 |   {
574 |    "cell_type": "markdown",
575 |    "id": "bfc0b019",
576 |    "metadata": {},
577 |    "source": [
578 |     "We will use 75% of our dataset for training and 25% for testing :"
579 |    ]
580 |   },
581 |   {
582 |    "cell_type": "code",
583 |    "execution_count": 11,
584 |    "id": "8a988b38",
585 |    "metadata": {},
586 |    "outputs": [],
587 |    "source": [
588 |     "features_train, features_test, ifMalware_train, ifMalware_test = train_test_split(features, ifMalware, test_size=0.25)"
589 |    ]
590 |   },
591 |   {
592 |    "cell_type": "markdown",
593 |    "id": "834ffef4",
594 |    "metadata": {},
595 |    "source": [
596 |     "Learning and Predict :"
597 |    ]
598 |   },
599 |   {
600 |    "cell_type": "code",
601 |    "execution_count": 12,
602 |    "id": "f18cafe1",
603 |    "metadata": {},
604 |    "outputs": [],
605 |    "source": [
606 |     "rfModel = RandomForestClassifier() # Defined the model."
607 |    ]
608 |   },
609 |   {
610 |    "cell_type": "code",
611 |    "execution_count": 13,
612 |    "id": "a8b11bd3",
613 |    "metadata": {},
614 |    "outputs": [
615 |     {
616 |      "data": {
617 |       "text/plain": [
618 |        "RandomForestClassifier()"
619 |       ]
620 |      },
621 |      "execution_count": 13,
622 |      "metadata": {},
623 |      "output_type": "execute_result"
624 |     }
625 |    ],
626 |    "source": [
627 |     "rfModel.fit(features_train, ifMalware_train) # Provided training data."
628 |    ]
629 |   },
630 |   {
631 |    "cell_type": "code",
632 |    "execution_count": 14,
633 |    "id": "4e41403c",
634 |    "metadata": {},
635 |    "outputs": [],
636 |    "source": [
637 |     "rfPredict = rfModel.predict(features_test) # Give the test data then call predict."
638 |    ]
639 |   },
640 |   {
641 |    "cell_type": "markdown",
642 |    "id": "42a29bb3",
643 |    "metadata": {},
644 |    "source": [
645 |     "Results :"
646 |    ]
647 |   },
648 |   {
649 |    "cell_type": "code",
650 |    "execution_count": 15,
651 |    "id": "310395c3",
652 |    "metadata": {},
653 |    "outputs": [
654 |     {
655 |      "name": "stdout",
656 |      "output_type": "stream",
657 |      "text": [
658 |       "Number of mislabeled out of a total of 34361 test entries: 301\n"
659 |      ]
660 |     }
661 |    ],
662 |    "source": [
663 |     "print(\"Number of mislabeled out of a total of %d test entries: %d\" % (features_test.shape[0], \n",
664 |     "                                                                      (ifMalware_test != rfPredict).sum()))"
665 |    ]
666 |   },
667 |   {
668 |    "cell_type": "code",
669 |    "execution_count": 16,
670 |    "id": "3da4e570",
671 |    "metadata": {},
672 |    "outputs": [],
673 |    "source": [
674 |     "successRate = 100 * f1_score(ifMalware_test, rfPredict, average='micro') # Success rate calculation"
675 |    ]
676 |   },
677 |   {
678 |    "cell_type": "code",
679 |    "execution_count": 17,
680 |    "id": "9a79bdd6",
681 |    "metadata": {},
682 |    "outputs": [
683 |     {
684 |      "name": "stdout",
685 |      "output_type": "stream",
686 |      "text": [
687 |       "The Success Rate was calculated as % : 99.1240068682518 with the Random Forest\n"
688 |      ]
689 |     }
690 |    ],
691 |    "source": [
692 |     "print(\"The Success Rate was calculated as % : \" + str(successRate) + \" with the Random Forest\")"
693 |    ]
694 |   },
695 |   {
696 |    "cell_type": "markdown",
697 |    "id": "fc87041f",
698 |    "metadata": {},
699 |    "source": [
700 |     "Visualization :"
701 |    ]
702 |   },
703 |   {
704 |    "cell_type": "code",
705 |    "execution_count": 18,
706 |    "id": "b5fb046a",
707 |    "metadata": {},
708 |    "outputs": [
709 |     {
710 |      "data": {
711 |       "text/plain": [
712 |        "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x120a61af0>"
713 |       ]
714 |      },
715 |      "execution_count": 18,
716 |      "metadata": {},
717 |      "output_type": "execute_result"
718 |     },
719 |     {
720 |      "data": {
721 |       "image/png": "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\n",
722 |       "text/plain": [
723 |        "<Figure size 432x288 with 2 Axes>"
724 |       ]
725 |      },
726 |      "metadata": {
727 |       "needs_background": "light"
728 |      },
729 |      "output_type": "display_data"
730 |     }
731 |    ],
732 |    "source": [
733 |     "plot_confusion_matrix(rfModel,features_test,ifMalware_test)"
734 |    ]
735 |   },
736 |   {
737 |    "cell_type": "code",
738 |    "execution_count": 19,
739 |    "id": "326f5f69",
740 |    "metadata": {},
741 |    "outputs": [
742 |     {
743 |      "data": {
744 |       "text/plain": [
745 |        "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x103fdb460>"
746 |       ]
747 |      },
748 |      "execution_count": 19,
749 |      "metadata": {},
750 |      "output_type": "execute_result"
751 |     },
752 |     {
753 |      "data": {
754 |       "image/png": 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\n",
755 |       "text/plain": [
756 |        "<Figure size 432x288 with 1 Axes>"
757 |       ]
758 |      },
759 |      "metadata": {
760 |       "needs_background": "light"
761 |      },
762 |      "output_type": "display_data"
763 |     }
764 |    ],
765 |    "source": [
766 |     "plot_precision_recall_curve(rfModel,features_test,ifMalware_test)"
767 |    ]
768 |   },
769 |   {
770 |    "cell_type": "code",
771 |    "execution_count": 20,
772 |    "id": "61719e65",
773 |    "metadata": {},
774 |    "outputs": [
775 |     {
776 |      "data": {
777 |       "text/plain": [
778 |        "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x121c11fd0>"
779 |       ]
780 |      },
781 |      "execution_count": 20,
782 |      "metadata": {},
783 |      "output_type": "execute_result"
784 |     },
785 |     {
786 |      "data": {
787 |       "image/png": 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\n",
788 |       "text/plain": [
789 |        "<Figure size 432x288 with 1 Axes>"
790 |       ]
791 |      },
792 |      "metadata": {
793 |       "needs_background": "light"
794 |      },
795 |      "output_type": "display_data"
796 |     }
797 |    ],
798 |    "source": [
799 |     "plot_roc_curve(rfModel,features_test,ifMalware_test)"
800 |    ]
801 |   },
802 |   {
803 |    "cell_type": "code",
804 |    "execution_count": null,
805 |    "id": "8ec51c31",
806 |    "metadata": {},
807 |    "outputs": [],
808 |    "source": []
809 |   }
810 |  ],
811 |  "metadata": {
812 |   "kernelspec": {
813 |    "display_name": "Python 3 (ipykernel)",
814 |    "language": "python",
815 |    "name": "python3"
816 |   },
817 |   "language_info": {
818 |    "codemirror_mode": {
819 |     "name": "ipython",
820 |     "version": 3
821 |    },
822 |    "file_extension": ".py",
823 |    "mimetype": "text/x-python",
824 |    "name": "python",
825 |    "nbconvert_exporter": "python",
826 |    "pygments_lexer": "ipython3",
827 |    "version": "3.9.10"
828 |   }
829 |  },
830 |  "nbformat": 4,
831 |  "nbformat_minor": 5
832 | }
833 | 


--------------------------------------------------------------------------------
/data-set/dataset.csv:
--------------------------------------------------------------------------------
 1 | AddressOfEntryPoint,MajorLinkerVersion,MajorImageVersion,MajorOperatingSystemVersion,DllCharacteristics,SizeOfStackReserve,NumberOfSections,ResourceSize,IfMalware
 2 | 47184,5,0,4,0,1048576,3,0,1
 3 | 257184,6,0,4,0,1048576,4,1720,1
 4 | 443086,11,0,4,34112,1048576,3,24192,1
 5 | 29959,6,0,4,0,1048576,5,7816,1
 6 | 636583,9,0,5,32768,1048576,3,0,1
 7 | 11344,14,0,6,33088,1048576,5,0,1
 8 | 33303,11,0,5,33088,1048576,5,2204424,1
 9 | 114288,6,0,4,0,1048576,3,928,1
10 | 15803,2,1,4,0,1048576,8,76424,1
11 | 662982,48,0,4,34144,1048576,3,1452,1
12 | 265614,8,0,4,34112,1048576,3,2560,1
13 | 261598,8,0,4,34112,1048576,3,2560,1
14 | 41910,10,0,5,33792,1048576,6,22770,1
15 | 7168,6,1,4,0,1048576,3,1432,1
16 | 226176,6,0,4,0,1048576,6,12928,1
17 | 32057,10,0,5,320,1048576,5,247608,1
18 | 176272,9,0,5,33024,1048576,3,4156,1
19 | 660302,48,0,4,34112,1048576,3,21188,1
20 | 14124,6,0,4,0,1048576,3,0,1
21 | 21982,6,0,4,0,1048576,4,17744,1
22 | 14764,2,6,4,32768,2097152,7,17800,1
23 | 609738,8,0,4,34112,1048576,3,118113,1
24 | 5936,10,0,5,34112,1048576,5,1628,1
25 | 9072,10,0,5,34112,1048576,4,265392,1
26 | 100370,7,5,5,32768,262144,2,0,1
27 | 5269,8,6,4,34112,1048576,7,8288,1
28 | 6264,14,6,6,33120,1048576,6,209496,1
29 | 924368,10,0,5,33088,1048576,3,1324,1
30 | 30650,6,0,4,0,1048576,4,3448736,1
31 | 324832,9,0,5,32768,1048576,4,50088,1
32 | 19001,14,0,6,320,1048576,6,856,1
33 | 664,6,0,4,0,1048576,4,26536,1
34 | 2564766,8,0,4,34112,1048576,3,371870,1
35 | 16896,5,0,4,0,1048576,4,2400,1
36 | 13199,6,6,4,34112,1048576,5,131952,0
37 | 27506,8,6,6,33088,262144,4,25695624,0
38 | 301411,14,0,6,33088,1048576,5,29024,0
39 | 30100,6,0,4,256,1048576,4,4072,0
40 | 40594,14,0,6,33088,1048576,5,25257092,0
41 | 15920,2,6,4,34144,2097152,9,38168,0
42 | 13784,6,6,4,34112,1048576,5,295776,0
43 | 4096,0,0,4,0,1048576,5,15104,0
44 | 13238,6,6,4,34112,1048576,5,75880,0
45 | 745196,2,6,6,33088,1048576,10,51196,0
46 | 43787,14,0,6,33088,1048576,5,135743656,0
47 | 261455,14,0,6,33024,1048576,4,52160,0
48 | 745196,2,6,6,33088,1048576,10,65828,0
49 | 4768,2,1,4,1344,2097152,11,174736,0
50 | 46622,14,0,6,33088,1048576,7,82774692,0
51 | 108448,14,0,6,49472,1048576,5,37936,0
52 | 


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