├── 1-Data_pre-processing_CAN.ipynb
├── 2-CNN_Model_Development&Hyperparameter Optimization.ipynb
├── 3-Ensemble_Models-CAN.ipynb
├── CAN.png
├── LICENSE
├── Paper_2201.11812.pdf
├── README.md
├── data
    ├── Car_Hacking_5%.csv
    └── README.md
├── framework.png
└── supplementary_code
    ├── CAR_IDS_SVC.ipynb
    └── README.md


/1-Data_pre-processing_CAN.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles \n",
  8 |     "This is the code for the paper entitled \"**A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles**\" accepted in IEEE International Conference on Communications (IEEE ICC).  \n",
  9 |     "Authors: Li Yang (lyang339@uwo.ca) and Abdallah Shami (Abdallah.Shami@uwo.ca)  \n",
 10 |     "Organization: The Optimized Computing and Communications (OC2) Lab, ECE Department, Western University\n",
 11 |     "\n",
 12 |     "**Notebook 1: Data pre-processing**  \n",
 13 |     "Procedures:  \n",
 14 |     "  1): Read the dataset  \n",
 15 |     "  2): Transform the tabular data into images  \n",
 16 |     "  3): Display the transformed images  \n",
 17 |     "  4): Split the training and test set  "
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "markdown",
 22 |    "metadata": {},
 23 |    "source": [
 24 |     "## Import libraries"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 1,
 30 |    "metadata": {},
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "import numpy as np\n",
 34 |     "import pandas as pd\n",
 35 |     "import os\n",
 36 |     "import cv2\n",
 37 |     "import math\n",
 38 |     "import random\n",
 39 |     "import matplotlib.pyplot as plt\n",
 40 |     "import shutil\n",
 41 |     "from sklearn.preprocessing import QuantileTransformer\n",
 42 |     "from PIL import Image\n",
 43 |     "import warnings\n",
 44 |     "warnings.filterwarnings(\"ignore\")"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "markdown",
 49 |    "metadata": {},
 50 |    "source": [
 51 |     "## Read the Car-Hacking/CAN-Intrusion dataset\n",
 52 |     "The complete Car-Hacking dataset is publicly available at: https://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset  \n",
 53 |     "In this repository, due to the file size limit of GitHub, we use the 5% subset."
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": 3,
 59 |    "metadata": {
 60 |     "collapsed": true
 61 |    },
 62 |    "outputs": [],
 63 |    "source": [
 64 |     "#Read dataset\n",
 65 |     "df=pd.read_csv('data/Car_Hacking_5%.csv')"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "code",
 70 |    "execution_count": 4,
 71 |    "metadata": {
 72 |     "scrolled": true
 73 |    },
 74 |    "outputs": [
 75 |     {
 76 |      "data": {
 77 |       "text/html": [
 78 |        "<div>\n",
 79 |        "<style scoped>\n",
 80 |        "    .dataframe tbody tr th:only-of-type {\n",
 81 |        "        vertical-align: middle;\n",
 82 |        "    }\n",
 83 |        "\n",
 84 |        "    .dataframe tbody tr th {\n",
 85 |        "        vertical-align: top;\n",
 86 |        "    }\n",
 87 |        "\n",
 88 |        "    .dataframe thead th {\n",
 89 |        "        text-align: right;\n",
 90 |        "    }\n",
 91 |        "</style>\n",
 92 |        "<table border=\"1\" class=\"dataframe\">\n",
 93 |        "  <thead>\n",
 94 |        "    <tr style=\"text-align: right;\">\n",
 95 |        "      <th></th>\n",
 96 |        "      <th>CAN ID</th>\n",
 97 |        "      <th>DATA[0]</th>\n",
 98 |        "      <th>DATA[1]</th>\n",
 99 |        "      <th>DATA[2]</th>\n",
100 |        "      <th>DATA[3]</th>\n",
101 |        "      <th>DATA[4]</th>\n",
102 |        "      <th>DATA[5]</th>\n",
103 |        "      <th>DATA[6]</th>\n",
104 |        "      <th>DATA[7]</th>\n",
105 |        "      <th>Label</th>\n",
106 |        "    </tr>\n",
107 |        "  </thead>\n",
108 |        "  <tbody>\n",
109 |        "    <tr>\n",
110 |        "      <th>0</th>\n",
111 |        "      <td>1201</td>\n",
112 |        "      <td>41</td>\n",
113 |        "      <td>39</td>\n",
114 |        "      <td>39</td>\n",
115 |        "      <td>35</td>\n",
116 |        "      <td>0</td>\n",
117 |        "      <td>0</td>\n",
118 |        "      <td>0</td>\n",
119 |        "      <td>154</td>\n",
120 |        "      <td>R</td>\n",
121 |        "    </tr>\n",
122 |        "    <tr>\n",
123 |        "      <th>1</th>\n",
124 |        "      <td>809</td>\n",
125 |        "      <td>64</td>\n",
126 |        "      <td>187</td>\n",
127 |        "      <td>127</td>\n",
128 |        "      <td>20</td>\n",
129 |        "      <td>17</td>\n",
130 |        "      <td>32</td>\n",
131 |        "      <td>0</td>\n",
132 |        "      <td>20</td>\n",
133 |        "      <td>R</td>\n",
134 |        "    </tr>\n",
135 |        "    <tr>\n",
136 |        "      <th>2</th>\n",
137 |        "      <td>1349</td>\n",
138 |        "      <td>216</td>\n",
139 |        "      <td>0</td>\n",
140 |        "      <td>0</td>\n",
141 |        "      <td>136</td>\n",
142 |        "      <td>0</td>\n",
143 |        "      <td>0</td>\n",
144 |        "      <td>0</td>\n",
145 |        "      <td>0</td>\n",
146 |        "      <td>R</td>\n",
147 |        "    </tr>\n",
148 |        "    <tr>\n",
149 |        "      <th>3</th>\n",
150 |        "      <td>1201</td>\n",
151 |        "      <td>41</td>\n",
152 |        "      <td>39</td>\n",
153 |        "      <td>39</td>\n",
154 |        "      <td>35</td>\n",
155 |        "      <td>0</td>\n",
156 |        "      <td>0</td>\n",
157 |        "      <td>0</td>\n",
158 |        "      <td>154</td>\n",
159 |        "      <td>R</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>4</th>\n",
163 |        "      <td>2</td>\n",
164 |        "      <td>0</td>\n",
165 |        "      <td>0</td>\n",
166 |        "      <td>0</td>\n",
167 |        "      <td>0</td>\n",
168 |        "      <td>0</td>\n",
169 |        "      <td>3</td>\n",
170 |        "      <td>2</td>\n",
171 |        "      <td>228</td>\n",
172 |        "      <td>R</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>...</th>\n",
176 |        "      <td>...</td>\n",
177 |        "      <td>...</td>\n",
178 |        "      <td>...</td>\n",
179 |        "      <td>...</td>\n",
180 |        "      <td>...</td>\n",
181 |        "      <td>...</td>\n",
182 |        "      <td>...</td>\n",
183 |        "      <td>...</td>\n",
184 |        "      <td>...</td>\n",
185 |        "      <td>...</td>\n",
186 |        "    </tr>\n",
187 |        "    <tr>\n",
188 |        "      <th>818435</th>\n",
189 |        "      <td>848</td>\n",
190 |        "      <td>5</td>\n",
191 |        "      <td>32</td>\n",
192 |        "      <td>52</td>\n",
193 |        "      <td>104</td>\n",
194 |        "      <td>117</td>\n",
195 |        "      <td>0</td>\n",
196 |        "      <td>0</td>\n",
197 |        "      <td>12</td>\n",
198 |        "      <td>R</td>\n",
199 |        "    </tr>\n",
200 |        "    <tr>\n",
201 |        "      <th>818436</th>\n",
202 |        "      <td>1088</td>\n",
203 |        "      <td>255</td>\n",
204 |        "      <td>0</td>\n",
205 |        "      <td>0</td>\n",
206 |        "      <td>0</td>\n",
207 |        "      <td>255</td>\n",
208 |        "      <td>134</td>\n",
209 |        "      <td>9</td>\n",
210 |        "      <td>0</td>\n",
211 |        "      <td>R</td>\n",
212 |        "    </tr>\n",
213 |        "    <tr>\n",
214 |        "      <th>818437</th>\n",
215 |        "      <td>848</td>\n",
216 |        "      <td>5</td>\n",
217 |        "      <td>32</td>\n",
218 |        "      <td>100</td>\n",
219 |        "      <td>104</td>\n",
220 |        "      <td>117</td>\n",
221 |        "      <td>0</td>\n",
222 |        "      <td>0</td>\n",
223 |        "      <td>92</td>\n",
224 |        "      <td>R</td>\n",
225 |        "    </tr>\n",
226 |        "    <tr>\n",
227 |        "      <th>818438</th>\n",
228 |        "      <td>1349</td>\n",
229 |        "      <td>216</td>\n",
230 |        "      <td>90</td>\n",
231 |        "      <td>0</td>\n",
232 |        "      <td>137</td>\n",
233 |        "      <td>0</td>\n",
234 |        "      <td>0</td>\n",
235 |        "      <td>0</td>\n",
236 |        "      <td>0</td>\n",
237 |        "      <td>R</td>\n",
238 |        "    </tr>\n",
239 |        "    <tr>\n",
240 |        "      <th>818439</th>\n",
241 |        "      <td>790</td>\n",
242 |        "      <td>5</td>\n",
243 |        "      <td>33</td>\n",
244 |        "      <td>48</td>\n",
245 |        "      <td>10</td>\n",
246 |        "      <td>33</td>\n",
247 |        "      <td>30</td>\n",
248 |        "      <td>0</td>\n",
249 |        "      <td>111</td>\n",
250 |        "      <td>R</td>\n",
251 |        "    </tr>\n",
252 |        "  </tbody>\n",
253 |        "</table>\n",
254 |        "<p>818440 rows × 10 columns</p>\n",
255 |        "</div>"
256 |       ],
257 |       "text/plain": [
258 |        "        CAN ID  DATA[0]  DATA[1]  DATA[2]  DATA[3]  DATA[4]  DATA[5]  DATA[6]  \\\n",
259 |        "0         1201       41       39       39       35        0        0        0   \n",
260 |        "1          809       64      187      127       20       17       32        0   \n",
261 |        "2         1349      216        0        0      136        0        0        0   \n",
262 |        "3         1201       41       39       39       35        0        0        0   \n",
263 |        "4            2        0        0        0        0        0        3        2   \n",
264 |        "...        ...      ...      ...      ...      ...      ...      ...      ...   \n",
265 |        "818435     848        5       32       52      104      117        0        0   \n",
266 |        "818436    1088      255        0        0        0      255      134        9   \n",
267 |        "818437     848        5       32      100      104      117        0        0   \n",
268 |        "818438    1349      216       90        0      137        0        0        0   \n",
269 |        "818439     790        5       33       48       10       33       30        0   \n",
270 |        "\n",
271 |        "        DATA[7] Label  \n",
272 |        "0           154     R  \n",
273 |        "1            20     R  \n",
274 |        "2             0     R  \n",
275 |        "3           154     R  \n",
276 |        "4           228     R  \n",
277 |        "...         ...   ...  \n",
278 |        "818435       12     R  \n",
279 |        "818436        0     R  \n",
280 |        "818437       92     R  \n",
281 |        "818438        0     R  \n",
282 |        "818439      111     R  \n",
283 |        "\n",
284 |        "[818440 rows x 10 columns]"
285 |       ]
286 |      },
287 |      "execution_count": 4,
288 |      "metadata": {},
289 |      "output_type": "execute_result"
290 |     }
291 |    ],
292 |    "source": [
293 |     "df"
294 |    ]
295 |   },
296 |   {
297 |    "cell_type": "code",
298 |    "execution_count": 5,
299 |    "metadata": {},
300 |    "outputs": [
301 |     {
302 |      "data": {
303 |       "text/plain": [
304 |        "R        701832\n",
305 |        "RPM       32539\n",
306 |        "gear      29944\n",
307 |        "DoS       29501\n",
308 |        "Fuzzy     24624\n",
309 |        "Name: Label, dtype: int64"
310 |       ]
311 |      },
312 |      "execution_count": 5,
313 |      "metadata": {},
314 |      "output_type": "execute_result"
315 |     }
316 |    ],
317 |    "source": [
318 |     "# The labels of the dataset. \"R\" indicates normal patterns, and there are four types of attack (DoS, fuzzy. gear spoofing, and RPM spoofing zttacks)\n",
319 |     "df.Label.value_counts()"
320 |    ]
321 |   },
322 |   {
323 |    "cell_type": "markdown",
324 |    "metadata": {
325 |     "collapsed": true
326 |    },
327 |    "source": [
328 |     "## Data Transformation\n",
329 |     "Convert tabular data to images\n",
330 |     "Procedures:\n",
331 |     "1. Use quantile transform to transform the original data samples into the scale of [0,255], representing pixel values\n",
332 |     "2. Generate images for each category (Normal, DoS, Fuzzy, Gear, RPM), each image consists of 27 data samples with 9 features. Thus, the size of each image is 9*9*3, length 9, width 9, and 3 color channels (RGB)."
333 |    ]
334 |   },
335 |   {
336 |    "cell_type": "code",
337 |    "execution_count": 6,
338 |    "metadata": {
339 |     "collapsed": true
340 |    },
341 |    "outputs": [],
342 |    "source": [
343 |     "# Transform all features into the scale of [0,1]\n",
344 |     "numeric_features = df.dtypes[df.dtypes != 'object'].index\n",
345 |     "scaler = QuantileTransformer() \n",
346 |     "df[numeric_features] = scaler.fit_transform(df[numeric_features])"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "code",
351 |    "execution_count": 7,
352 |    "metadata": {},
353 |    "outputs": [],
354 |    "source": [
355 |     "# Multiply the feature values by 255 to transform them into the scale of [0,255]\n",
356 |     "df[numeric_features] = df[numeric_features].apply(\n",
357 |     "    lambda x: (x*255))"
358 |    ]
359 |   },
360 |   {
361 |    "cell_type": "code",
362 |    "execution_count": 8,
363 |    "metadata": {},
364 |    "outputs": [
365 |     {
366 |      "data": {
367 |       "text/html": [
368 |        "<div>\n",
369 |        "<style scoped>\n",
370 |        "    .dataframe tbody tr th:only-of-type {\n",
371 |        "        vertical-align: middle;\n",
372 |        "    }\n",
373 |        "\n",
374 |        "    .dataframe tbody tr th {\n",
375 |        "        vertical-align: top;\n",
376 |        "    }\n",
377 |        "\n",
378 |        "    .dataframe thead th {\n",
379 |        "        text-align: right;\n",
380 |        "    }\n",
381 |        "</style>\n",
382 |        "<table border=\"1\" class=\"dataframe\">\n",
383 |        "  <thead>\n",
384 |        "    <tr style=\"text-align: right;\">\n",
385 |        "      <th></th>\n",
386 |        "      <th>CAN ID</th>\n",
387 |        "      <th>DATA[0]</th>\n",
388 |        "      <th>DATA[1]</th>\n",
389 |        "      <th>DATA[2]</th>\n",
390 |        "      <th>DATA[3]</th>\n",
391 |        "      <th>DATA[4]</th>\n",
392 |        "      <th>DATA[5]</th>\n",
393 |        "      <th>DATA[6]</th>\n",
394 |        "      <th>DATA[7]</th>\n",
395 |        "    </tr>\n",
396 |        "  </thead>\n",
397 |        "  <tbody>\n",
398 |        "    <tr>\n",
399 |        "      <th>count</th>\n",
400 |        "      <td>818440.000000</td>\n",
401 |        "      <td>818440.000000</td>\n",
402 |        "      <td>818440.000000</td>\n",
403 |        "      <td>818440.000000</td>\n",
404 |        "      <td>818440.000000</td>\n",
405 |        "      <td>818440.000000</td>\n",
406 |        "      <td>818440.000000</td>\n",
407 |        "      <td>818440.000000</td>\n",
408 |        "      <td>818440.000000</td>\n",
409 |        "    </tr>\n",
410 |        "    <tr>\n",
411 |        "      <th>mean</th>\n",
412 |        "      <td>127.458603</td>\n",
413 |        "      <td>113.635407</td>\n",
414 |        "      <td>108.055500</td>\n",
415 |        "      <td>89.524039</td>\n",
416 |        "      <td>109.930495</td>\n",
417 |        "      <td>105.682464</td>\n",
418 |        "      <td>112.273096</td>\n",
419 |        "      <td>84.945440</td>\n",
420 |        "      <td>93.094805</td>\n",
421 |        "    </tr>\n",
422 |        "    <tr>\n",
423 |        "      <th>std</th>\n",
424 |        "      <td>73.780402</td>\n",
425 |        "      <td>89.993275</td>\n",
426 |        "      <td>93.448831</td>\n",
427 |        "      <td>100.589117</td>\n",
428 |        "      <td>103.632690</td>\n",
429 |        "      <td>95.716420</td>\n",
430 |        "      <td>90.993393</td>\n",
431 |        "      <td>101.365609</td>\n",
432 |        "      <td>100.186463</td>\n",
433 |        "    </tr>\n",
434 |        "    <tr>\n",
435 |        "      <th>min</th>\n",
436 |        "      <td>0.000000</td>\n",
437 |        "      <td>0.000000</td>\n",
438 |        "      <td>0.000000</td>\n",
439 |        "      <td>0.000000</td>\n",
440 |        "      <td>0.000000</td>\n",
441 |        "      <td>0.000000</td>\n",
442 |        "      <td>0.000000</td>\n",
443 |        "      <td>0.000000</td>\n",
444 |        "      <td>0.000000</td>\n",
445 |        "    </tr>\n",
446 |        "    <tr>\n",
447 |        "      <th>25%</th>\n",
448 |        "      <td>66.876877</td>\n",
449 |        "      <td>0.000000</td>\n",
450 |        "      <td>0.000000</td>\n",
451 |        "      <td>0.000000</td>\n",
452 |        "      <td>0.000000</td>\n",
453 |        "      <td>0.000000</td>\n",
454 |        "      <td>0.000000</td>\n",
455 |        "      <td>0.000000</td>\n",
456 |        "      <td>0.000000</td>\n",
457 |        "    </tr>\n",
458 |        "    <tr>\n",
459 |        "      <th>50%</th>\n",
460 |        "      <td>122.650150</td>\n",
461 |        "      <td>126.096096</td>\n",
462 |        "      <td>115.503003</td>\n",
463 |        "      <td>0.000000</td>\n",
464 |        "      <td>130.818318</td>\n",
465 |        "      <td>127.755255</td>\n",
466 |        "      <td>129.542042</td>\n",
467 |        "      <td>0.000000</td>\n",
468 |        "      <td>0.000000</td>\n",
469 |        "    </tr>\n",
470 |        "    <tr>\n",
471 |        "      <th>75%</th>\n",
472 |        "      <td>190.548048</td>\n",
473 |        "      <td>192.462462</td>\n",
474 |        "      <td>193.611111</td>\n",
475 |        "      <td>199.099099</td>\n",
476 |        "      <td>190.675676</td>\n",
477 |        "      <td>193.355856</td>\n",
478 |        "      <td>190.165165</td>\n",
479 |        "      <td>192.207207</td>\n",
480 |        "      <td>190.675676</td>\n",
481 |        "    </tr>\n",
482 |        "    <tr>\n",
483 |        "      <th>max</th>\n",
484 |        "      <td>255.000000</td>\n",
485 |        "      <td>255.000000</td>\n",
486 |        "      <td>255.000000</td>\n",
487 |        "      <td>255.000000</td>\n",
488 |        "      <td>255.000000</td>\n",
489 |        "      <td>255.000000</td>\n",
490 |        "      <td>255.000000</td>\n",
491 |        "      <td>255.000000</td>\n",
492 |        "      <td>255.000000</td>\n",
493 |        "    </tr>\n",
494 |        "  </tbody>\n",
495 |        "</table>\n",
496 |        "</div>"
497 |       ],
498 |       "text/plain": [
499 |        "              CAN ID        DATA[0]        DATA[1]        DATA[2]  \\\n",
500 |        "count  818440.000000  818440.000000  818440.000000  818440.000000   \n",
501 |        "mean      127.458603     113.635407     108.055500      89.524039   \n",
502 |        "std        73.780402      89.993275      93.448831     100.589117   \n",
503 |        "min         0.000000       0.000000       0.000000       0.000000   \n",
504 |        "25%        66.876877       0.000000       0.000000       0.000000   \n",
505 |        "50%       122.650150     126.096096     115.503003       0.000000   \n",
506 |        "75%       190.548048     192.462462     193.611111     199.099099   \n",
507 |        "max       255.000000     255.000000     255.000000     255.000000   \n",
508 |        "\n",
509 |        "             DATA[3]        DATA[4]        DATA[5]        DATA[6]  \\\n",
510 |        "count  818440.000000  818440.000000  818440.000000  818440.000000   \n",
511 |        "mean      109.930495     105.682464     112.273096      84.945440   \n",
512 |        "std       103.632690      95.716420      90.993393     101.365609   \n",
513 |        "min         0.000000       0.000000       0.000000       0.000000   \n",
514 |        "25%         0.000000       0.000000       0.000000       0.000000   \n",
515 |        "50%       130.818318     127.755255     129.542042       0.000000   \n",
516 |        "75%       190.675676     193.355856     190.165165     192.207207   \n",
517 |        "max       255.000000     255.000000     255.000000     255.000000   \n",
518 |        "\n",
519 |        "             DATA[7]  \n",
520 |        "count  818440.000000  \n",
521 |        "mean       93.094805  \n",
522 |        "std       100.186463  \n",
523 |        "min         0.000000  \n",
524 |        "25%         0.000000  \n",
525 |        "50%         0.000000  \n",
526 |        "75%       190.675676  \n",
527 |        "max       255.000000  "
528 |       ]
529 |      },
530 |      "execution_count": 8,
531 |      "metadata": {},
532 |      "output_type": "execute_result"
533 |     }
534 |    ],
535 |    "source": [
536 |     "df.describe()"
537 |    ]
538 |   },
539 |   {
540 |    "cell_type": "markdown",
541 |    "metadata": {
542 |     "collapsed": true
543 |    },
544 |    "source": [
545 |     "All features are in the same scale of [0,255]"
546 |    ]
547 |   },
548 |   {
549 |    "cell_type": "markdown",
550 |    "metadata": {},
551 |    "source": [
552 |     "### Generate images for each class"
553 |    ]
554 |   },
555 |   {
556 |    "cell_type": "code",
557 |    "execution_count": 9,
558 |    "metadata": {
559 |     "collapsed": true
560 |    },
561 |    "outputs": [],
562 |    "source": [
563 |     "df0=df[df['Label']=='R'].drop(['Label'],axis=1)\n",
564 |     "df1=df[df['Label']=='RPM'].drop(['Label'],axis=1)\n",
565 |     "df2=df[df['Label']=='gear'].drop(['Label'],axis=1)\n",
566 |     "df3=df[df['Label']=='DoS'].drop(['Label'],axis=1)\n",
567 |     "df4=df[df['Label']=='Fuzzy'].drop(['Label'],axis=1)"
568 |    ]
569 |   },
570 |   {
571 |    "cell_type": "code",
572 |    "execution_count": 30,
573 |    "metadata": {},
574 |    "outputs": [],
575 |    "source": [
576 |     "# Generate 9*9 color images for class 0 (Normal)\n",
577 |     "count=0\n",
578 |     "ims = []\n",
579 |     "\n",
580 |     "image_path = \"train/0/\"\n",
581 |     "os.makedirs(image_path)\n",
582 |     "\n",
583 |     "for i in range(0, len(df0)):  \n",
584 |     "    count=count+1\n",
585 |     "    if count<=27: \n",
586 |     "        im=df0.iloc[i].values\n",
587 |     "        ims=np.append(ims,im)\n",
588 |     "    else:\n",
589 |     "        ims=np.array(ims).reshape(9,9,3)\n",
590 |     "        array = np.array(ims, dtype=np.uint8)\n",
591 |     "        new_image = Image.fromarray(array)\n",
592 |     "        new_image.save(image_path+str(i)+'.png')\n",
593 |     "        count=0\n",
594 |     "        ims = []"
595 |    ]
596 |   },
597 |   {
598 |    "cell_type": "code",
599 |    "execution_count": 31,
600 |    "metadata": {},
601 |    "outputs": [],
602 |    "source": [
603 |     "# Generate 9*9 color images for class 1 (RPM spoofing)\n",
604 |     "count=0\n",
605 |     "ims = []\n",
606 |     "\n",
607 |     "image_path = \"train/1/\"\n",
608 |     "os.makedirs(image_path)\n",
609 |     "\n",
610 |     "for i in range(0, len(df1)):  \n",
611 |     "    count=count+1\n",
612 |     "    if count<=27: \n",
613 |     "        im=df1.iloc[i].values\n",
614 |     "        ims=np.append(ims,im)\n",
615 |     "    else:\n",
616 |     "        ims=np.array(ims).reshape(9,9,3)\n",
617 |     "        array = np.array(ims, dtype=np.uint8)\n",
618 |     "        new_image = Image.fromarray(array)\n",
619 |     "        new_image.save(image_path+str(i)+'.png')\n",
620 |     "        count=0\n",
621 |     "        ims = []"
622 |    ]
623 |   },
624 |   {
625 |    "cell_type": "code",
626 |    "execution_count": 33,
627 |    "metadata": {},
628 |    "outputs": [],
629 |    "source": [
630 |     "# Generate 9*9 color images for class 2 (Gear spoofing)\n",
631 |     "count=0\n",
632 |     "ims = []\n",
633 |     "\n",
634 |     "image_path = \"train/2/\"\n",
635 |     "os.makedirs(image_path)\n",
636 |     "\n",
637 |     "for i in range(0, len(df2)):  \n",
638 |     "    count=count+1\n",
639 |     "    if count<=27: \n",
640 |     "        im=df2.iloc[i].values\n",
641 |     "        ims=np.append(ims,im)\n",
642 |     "    else:\n",
643 |     "        ims=np.array(ims).reshape(9,9,3)\n",
644 |     "        array = np.array(ims, dtype=np.uint8)\n",
645 |     "        new_image = Image.fromarray(array)\n",
646 |     "        new_image.save(image_path+str(i)+'.png')\n",
647 |     "        count=0\n",
648 |     "        ims = []"
649 |    ]
650 |   },
651 |   {
652 |    "cell_type": "code",
653 |    "execution_count": 34,
654 |    "metadata": {
655 |     "collapsed": true
656 |    },
657 |    "outputs": [],
658 |    "source": [
659 |     "# Generate 9*9 color images for class 3 (DoS attack)\n",
660 |     "count=0\n",
661 |     "ims = []\n",
662 |     "\n",
663 |     "image_path = \"train/3/\"\n",
664 |     "os.makedirs(image_path)\n",
665 |     "\n",
666 |     "\n",
667 |     "for i in range(0, len(df3)):  \n",
668 |     "    count=count+1\n",
669 |     "    if count<=27: \n",
670 |     "        im=df3.iloc[i].values\n",
671 |     "        ims=np.append(ims,im)\n",
672 |     "    else:\n",
673 |     "        ims=np.array(ims).reshape(9,9,3)\n",
674 |     "        array = np.array(ims, dtype=np.uint8)\n",
675 |     "        new_image = Image.fromarray(array)\n",
676 |     "        new_image.save(image_path+str(i)+'.png')\n",
677 |     "        count=0\n",
678 |     "        ims = []"
679 |    ]
680 |   },
681 |   {
682 |    "cell_type": "code",
683 |    "execution_count": 35,
684 |    "metadata": {
685 |     "collapsed": true
686 |    },
687 |    "outputs": [],
688 |    "source": [
689 |     "# Generate 9*9 color images for class 4 (Fuzzy attack)\n",
690 |     "count=0\n",
691 |     "ims = []\n",
692 |     "\n",
693 |     "image_path = \"train/4/\"\n",
694 |     "os.makedirs(image_path)\n",
695 |     "\n",
696 |     "\n",
697 |     "for i in range(0, len(df4)):  \n",
698 |     "    count=count+1\n",
699 |     "    if count<=27: \n",
700 |     "        im=df4.iloc[i].values\n",
701 |     "        ims=np.append(ims,im)\n",
702 |     "    else:\n",
703 |     "        ims=np.array(ims).reshape(9,9,3)\n",
704 |     "        array = np.array(ims, dtype=np.uint8)\n",
705 |     "        new_image = Image.fromarray(array)\n",
706 |     "        new_image.save(image_path+str(i)+'.png')\n",
707 |     "        count=0\n",
708 |     "        ims = []"
709 |    ]
710 |   },
711 |   {
712 |    "cell_type": "markdown",
713 |    "metadata": {},
714 |    "source": [
715 |     "## Split the training and test set "
716 |    ]
717 |   },
718 |   {
719 |    "cell_type": "code",
720 |    "execution_count": 56,
721 |    "metadata": {},
722 |    "outputs": [
723 |     {
724 |      "name": "stdout",
725 |      "output_type": "stream",
726 |      "text": [
727 |       "29227\n"
728 |      ]
729 |     }
730 |    ],
731 |    "source": [
732 |     "# Create folders to store images\n",
733 |     "Train_Dir='./train/'\n",
734 |     "Val_Dir='./test/'\n",
735 |     "allimgs=[]\n",
736 |     "for subdir in os.listdir(Train_Dir):\n",
737 |     "    for filename in os.listdir(os.path.join(Train_Dir,subdir)):\n",
738 |     "        filepath=os.path.join(Train_Dir,subdir,filename)\n",
739 |     "        allimgs.append(filepath)\n",
740 |     "print(len(allimgs)) # Print the total number of images"
741 |    ]
742 |   },
743 |   {
744 |    "cell_type": "code",
745 |    "execution_count": 58,
746 |    "metadata": {},
747 |    "outputs": [],
748 |    "source": [
749 |     "#split a test set from the dataset, train/test size = 80%/20%\n",
750 |     "Numbers=len(allimgs)//5 \t#size of test set (20%)\n",
751 |     "\n",
752 |     "def mymovefile(srcfile,dstfile):\n",
753 |     "    if not os.path.isfile(srcfile):\n",
754 |     "        print (\"%s not exist!\"%(srcfile))\n",
755 |     "    else:\n",
756 |     "        fpath,fname=os.path.split(dstfile)    \n",
757 |     "        if not os.path.exists(fpath):\n",
758 |     "            os.makedirs(fpath)               \n",
759 |     "        shutil.move(srcfile,dstfile)          \n",
760 |     "        #print (\"move %s -> %s\"%(srcfile,dstfile))"
761 |    ]
762 |   },
763 |   {
764 |    "cell_type": "code",
765 |    "execution_count": 59,
766 |    "metadata": {
767 |     "scrolled": true
768 |    },
769 |    "outputs": [
770 |     {
771 |      "data": {
772 |       "text/plain": [
773 |        "5845"
774 |       ]
775 |      },
776 |      "execution_count": 59,
777 |      "metadata": {},
778 |      "output_type": "execute_result"
779 |     }
780 |    ],
781 |    "source": [
782 |     "# The size of test set\n",
783 |     "Numbers"
784 |    ]
785 |   },
786 |   {
787 |    "cell_type": "code",
788 |    "execution_count": 60,
789 |    "metadata": {},
790 |    "outputs": [
791 |     {
792 |      "name": "stdout",
793 |      "output_type": "stream",
794 |      "text": [
795 |       "Finish creating test set\n"
796 |      ]
797 |     }
798 |    ],
799 |    "source": [
800 |     "# Create the test set\n",
801 |     "val_imgs=random.sample(allimgs,Numbers)\n",
802 |     "for img in val_imgs:\n",
803 |     "    dest_path=img.replace(Train_Dir,Val_Dir)\n",
804 |     "    mymovefile(img,dest_path)\n",
805 |     "print('Finish creating test set')"
806 |    ]
807 |   },
808 |   {
809 |    "cell_type": "code",
810 |    "execution_count": 61,
811 |    "metadata": {
812 |     "collapsed": true
813 |    },
814 |    "outputs": [],
815 |    "source": [
816 |     "#resize the images 224*224 for better CNN training\n",
817 |     "def get_224(folder,dstdir):\n",
818 |     "    imgfilepaths=[]\n",
819 |     "    for root,dirs,imgs in os.walk(folder):\n",
820 |     "        for thisimg in imgs:\n",
821 |     "            thisimg_path=os.path.join(root,thisimg)\n",
822 |     "            imgfilepaths.append(thisimg_path)\n",
823 |     "    for thisimg_path in imgfilepaths:\n",
824 |     "        dir_name,filename=os.path.split(thisimg_path)\n",
825 |     "        dir_name=dir_name.replace(folder,dstdir)\n",
826 |     "        new_file_path=os.path.join(dir_name,filename)\n",
827 |     "        if not os.path.exists(dir_name):\n",
828 |     "            os.makedirs(dir_name)\n",
829 |     "        img=cv2.imread(thisimg_path)\n",
830 |     "        img=cv2.resize(img,(224,224))\n",
831 |     "        cv2.imwrite(new_file_path,img)\n",
832 |     "    print('Finish resizing'.format(folder=folder))"
833 |    ]
834 |   },
835 |   {
836 |    "cell_type": "code",
837 |    "execution_count": 62,
838 |    "metadata": {},
839 |    "outputs": [
840 |     {
841 |      "name": "stdout",
842 |      "output_type": "stream",
843 |      "text": [
844 |       "Finish resizing\n"
845 |      ]
846 |     }
847 |    ],
848 |    "source": [
849 |     "DATA_DIR_224='./train_224/'\n",
850 |     "get_224(folder='./train/',dstdir=DATA_DIR_224)"
851 |    ]
852 |   },
853 |   {
854 |    "cell_type": "code",
855 |    "execution_count": 63,
856 |    "metadata": {},
857 |    "outputs": [
858 |     {
859 |      "name": "stdout",
860 |      "output_type": "stream",
861 |      "text": [
862 |       "Finish resizing\n"
863 |      ]
864 |     }
865 |    ],
866 |    "source": [
867 |     "DATA_DIR2_224='./test_224/'\n",
868 |     "get_224(folder='./test/',dstdir=DATA_DIR2_224)"
869 |    ]
870 |   },
871 |   {
872 |    "cell_type": "markdown",
873 |    "metadata": {},
874 |    "source": [
875 |     "### Display samples for each category"
876 |    ]
877 |   },
878 |   {
879 |    "cell_type": "code",
880 |    "execution_count": 2,
881 |    "metadata": {},
882 |    "outputs": [
883 |     {
884 |      "data": {
885 |       "image/png": 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\n",
886 |       "text/plain": [
887 |        "<Figure size 720x720 with 5 Axes>"
888 |       ]
889 |      },
890 |      "metadata": {
891 |       "needs_background": "light"
892 |      },
893 |      "output_type": "display_data"
894 |     }
895 |    ],
896 |    "source": [
897 |     "# Read the images for each category, the file name may vary (27.png, 83.png...)\n",
898 |     "img1 = Image.open('./train_224/0/27.png')\n",
899 |     "img2 = Image.open('./train_224/1/83.png')\n",
900 |     "img3 = Image.open('./train_224/2/27.png')\n",
901 |     "img4 = Image.open('./train_224/3/27.png')\n",
902 |     "img5 = Image.open('./train_224/4/27.png')\n",
903 |     "\n",
904 |     "plt.figure(figsize=(10, 10)) \n",
905 |     "plt.subplot(1,5,1)\n",
906 |     "plt.imshow(img1)\n",
907 |     "plt.title(\"Normal\")\n",
908 |     "plt.subplot(1,5,2)\n",
909 |     "plt.imshow(img2)\n",
910 |     "plt.title(\"RPM Spoofing\")\n",
911 |     "plt.subplot(1,5,3)\n",
912 |     "plt.imshow(img3)\n",
913 |     "plt.title(\"Gear Spoofing\")\n",
914 |     "plt.subplot(1,5,4)\n",
915 |     "plt.imshow(img4)\n",
916 |     "plt.title(\"DoS Attack\")\n",
917 |     "plt.subplot(1,5,5)\n",
918 |     "plt.imshow(img5)\n",
919 |     "plt.title(\"Fuzzy Attack\")\n",
920 |     "plt.show()  # display it"
921 |    ]
922 |   },
923 |   {
924 |    "cell_type": "code",
925 |    "execution_count": null,
926 |    "metadata": {},
927 |    "outputs": [],
928 |    "source": []
929 |   }
930 |  ],
931 |  "metadata": {
932 |   "anaconda-cloud": {},
933 |   "kernelspec": {
934 |    "display_name": "Python 3",
935 |    "language": "python",
936 |    "name": "python3"
937 |   },
938 |   "language_info": {
939 |    "codemirror_mode": {
940 |     "name": "ipython",
941 |     "version": 3
942 |    },
943 |    "file_extension": ".py",
944 |    "mimetype": "text/x-python",
945 |    "name": "python",
946 |    "nbconvert_exporter": "python",
947 |    "pygments_lexer": "ipython3",
948 |    "version": "3.6.8"
949 |   }
950 |  },
951 |  "nbformat": 4,
952 |  "nbformat_minor": 2
953 | }
954 | 


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/CAN.png:
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https://raw.githubusercontent.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/c2aacb76cc184dc1ea29f2c6b97e5bbde8221f71/CAN.png


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2022 Western OC2 Lab
 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 | 


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/Paper_2201.11812.pdf:
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https://raw.githubusercontent.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/c2aacb76cc184dc1ea29f2c6b97e5bbde8221f71/Paper_2201.11812.pdf


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/README.md:
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 1 | # Intrusion-Detection-System-Using-CNN-and-Transfer-Learning
 2 | 
 3 | This is the code for the paper entitled "**[A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles](https://arxiv.org/pdf/2201.11812.pdf)**" published in **IEEE International Conference on Communications (IEEE ICC)**, doi: [10.1109/ICC45855.2022.9838780](https://ieeexplore.ieee.org/document/9838780).  
 4 | - Authors: Li Yang and Abdallah Shami  
 5 | - Organization: The Optimized Computing and Communications (OC2) Lab, ECE Department, Western University
 6 | 
 7 | This repository introduces how to use **convolutional neural networks (CNNs)** and **transfer learning** techniques to develop **intrusion detection systems**. **Ensemble learning** and **hyperparameter optimization techniques** are also used to achieve optimized model performance.
 8 | 
 9 | - Another **intrusion detection system development code** using **decision tree-based machine learning algorithms (Decision tree, random forest, XGBoost, stacking, etc.)** can be found in: [Intrusion-Detection-System-Using-Machine-Learning](https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-Machine-Learning)
10 | 
11 | - A comprehensive **hyperparameter optimization** tutorial code can be found in: [Hyperparameter-Optimization-of-Machine-Learning-Algorithms](https://github.com/LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms)
12 | 
13 | ## Abstract of The Paper
14 | Modern vehicles, including autonomous vehicles and connected vehicles,  are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.
15 | 
16 | <p float="left">
17 |   <img src="https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/blob/main/framework.png" width="500" />
18 |   <img src="https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/blob/main/CAN.png" width="400" /> 
19 | </p>
20 | 
21 | ## Implementation 
22 | ### CNN Models  
23 | * VGG16
24 | * VGG19
25 | * Xception
26 | * Inception
27 | * Resnet
28 | * InceptionResnet
29 | 
30 | ### Ensemble Learning Models
31 | * Bagging
32 | * Probability Averaging
33 | * Concatenation
34 | 
35 | ### Hyperparameter Optimization Methods
36 | * Random Search (RS)
37 | * Bayesian Optimization - Tree Parzen Estimator(BO-TPE)
38 | 
39 | ### Dataset 
40 | 1. CAN-intrusion/Car-Hacking dataset, a benchmark network security dataset for intra-vehicle intrusion detection
41 | * Publicly available at: https://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset  
42 | * Can be processed using the same code
43 | 
44 | 2. CICIDS2017 dataset, a popular network traffic dataset for intrusion detection problems
45 | * Publicly available at: https://www.unb.ca/cic/datasets/ids-2017.html  
46 | 
47 | For the purpose of displaying the experimental results in Jupyter Notebook, the sampled subset of the CAN-intrusion dataset is used in the sample code. The subsets are in the "[data](https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/tree/main/data)" folder.
48 | 
49 | ### Code  
50 | * [1-Data_pre-processing_CAN.ipynb](https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/blob/main/1-Data_pre-processing_CAN.ipynb): code for data pre-processing and transformation (tabular data to images).  
51 | * [2-CNN_Model_Development&Hyperparameter Optimization.ipynb](https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/blob/main/2-CNN_Model_Development%26Hyperparameter%20Optimization.ipynb): code for the development and CNN models and their hyperparameter optimization.
52 | * [3-Ensemble_Models-CAN.ipynb](https://github.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/blob/main/3-Ensemble_Models-CAN.ipynb): code for the construction of three ensemble learning techniques.
53 | 
54 | Libraries  
55 | * Python 3.5+
56 | * [Keras 2.1.0+](hhttps://keras.io/)  
57 | * [Tensorflow 1.10.0+](https://www.tensorflow.org/install/gpu)
58 | * [OpenCV-python](https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html)
59 | * [hyperopt](https://github.com/hyperopt/hyperopt) 
60 | 
61 | ## Contact-Info
62 | Please feel free to contact us for any questions or cooperation opportunities. We will be happy to help.
63 | * Email: [liyanghart@gmail.com](mailto:liyanghart@gmail.com) or [Abdallah.Shami@uwo.ca](mailto:Abdallah.Shami@uwo.ca)
64 | * GitHub: [LiYangHart](https://github.com/LiYangHart) and [Western OC2 Lab](https://github.com/Western-OC2-Lab/)
65 | * LinkedIn: [Li Yang](https://www.linkedin.com/in/li-yang-phd-65a190176/)  
66 | * Google Scholar: [Li Yang](https://scholar.google.com.eg/citations?user=XEfM7bIAAAAJ&hl=en) and [OC2 Lab](https://scholar.google.com.eg/citations?user=oiebNboAAAAJ&hl=en)
67 | 
68 | ## Citation
69 | If you find this repository useful in your research, please cite this article as:  
70 | 
71 | L. Yang and A. Shami, "A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles," ICC 2022 - IEEE International Conference on Communications, 2022, pp. 2774-2779, doi: 10.1109/ICC45855.2022.9838780.
72 | 
73 | ```
74 | @INPROCEEDINGS{9838780,
75 |   author={Yang, Li and Shami, Abdallah},
76 |   booktitle={ICC 2022 - IEEE International Conference on Communications}, 
77 |   title={A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles}, 
78 |   year={2022},
79 |   pages={2774-2779},
80 |   doi={10.1109/ICC45855.2022.9838780}}
81 | ```
82 | 


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/data/README.md:
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1 | # The sampled datasets used for the experiments in the sample code
2 | 
3 | **Car_Hacking_5%.csv**: The 5% randomly sampled subset of the [Car Hacking dataset](https://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset)  
4 | 


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/framework.png:
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https://raw.githubusercontent.com/Western-OC2-Lab/Intrusion-Detection-System-Using-CNN-and-Transfer-Learning/c2aacb76cc184dc1ea29f2c6b97e5bbde8221f71/framework.png


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/supplementary_code/CAR_IDS_SVC.ipynb:
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   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "CAR_IDS_LOGISTIC_SVM (1).ipynb",
   7 |       "provenance": [],
   8 |       "collapsed_sections": []
   9 |     },
  10 |     "kernelspec": {
  11 |       "name": "python3",
  12 |       "display_name": "Python 3"
  13 |     },
  14 |     "language_info": {
  15 |       "name": "python"
  16 |     }
  17 |   },
  18 |   "cells": [
  19 |     {
  20 |       "cell_type": "code",
  21 |       "execution_count": 1,
  22 |       "metadata": {
  23 |         "id": "lGiyP2dR6Jw-"
  24 |       },
  25 |       "outputs": [],
  26 |       "source": [
  27 |         "import numpy as np\n",
  28 |         "import pandas as pd\n",
  29 |         "from sklearn.linear_model import LogisticRegression\n",
  30 |         "from sklearn.ensemble import RandomForestClassifier"
  31 |       ]
  32 |     },
  33 |     {
  34 |       "cell_type": "code",
  35 |       "source": [
  36 |         "def changecolumn(dataset, AttackType):\n",
  37 |         "    df = pd.read_csv(dataset).sample(frac = 0.1, random_state = 20, replace = False).reset_index(drop=True)\n",
  38 |         "    df.columns = [\"Timestamp\", \"CAN ID\", \"Byte\", \"DATA[0]\",\"DATA[1]\",\"DATA[2]\",\"DATA[3]\",\"DATA[4]\",\"DATA[5]\",\"DATA[6]\",\"DATA[7]\",\"AttackType\"]\n",
  39 |         "    df['AttackType'] = np.where(df['AttackType'] == 'T',AttackType, 'Normal Message')\n",
  40 |         "    df.dropna()\n",
  41 |         "    return df\n",
  42 |         "\n",
  43 |         "dfDos = changecolumn('DoS_dataset.csv','DoS Attack')\n",
  44 |         "dfFuzzy = changecolumn('Fuzzy_dataset.csv','Fuzzy Attack')\n",
  45 |         "dfGear = changecolumn('gear_dataset.csv','Gear Spooing Attack')\n",
  46 |         "dfRPM = changecolumn('RPM_dataset.csv','RPM Spoofing Attack')\n",
  47 |         "frames = [dfDos, dfFuzzy, dfGear, dfRPM]\n",
  48 |         "df = pd.concat(frames)\n",
  49 |         "print(df.head(10))\n",
  50 |         "print(df.shape)\n"
  51 |       ],
  52 |       "metadata": {
  53 |         "colab": {
  54 |           "base_uri": "https://localhost:8080/"
  55 |         },
  56 |         "id": "K4qXdKi-756E",
  57 |         "outputId": "fcf9fee4-8ccb-48a1-99bc-401628828b0f"
  58 |       },
  59 |       "execution_count": 2,
  60 |       "outputs": [
  61 |         {
  62 |           "output_type": "stream",
  63 |           "name": "stdout",
  64 |           "text": [
  65 |             "      Timestamp CAN ID  Byte DATA[0] DATA[1] DATA[2] DATA[3] DATA[4] DATA[5]  \\\n",
  66 |             "0  1.478200e+09   0000     8      00      00      00      00      00      00   \n",
  67 |             "1  1.478201e+09   0131     8      1b      80      00      00      3f      7f   \n",
  68 |             "2  1.478199e+09   00a1     8      80      89      00      00      24      00   \n",
  69 |             "3  1.478200e+09   0260     8      18      21      22      30      08      8f   \n",
  70 |             "4  1.478201e+09   02c0     8      14      00      00      00      00      00   \n",
  71 |             "5  1.478200e+09   0130     8      0b      80      00      ff      08      80   \n",
  72 |             "6  1.478200e+09   0370     8      00      20      00      00      00      00   \n",
  73 |             "7  1.478199e+09   04f0     8      00      00      00      80      00      69   \n",
  74 |             "8  1.478199e+09   0130     8      05      80      00      ff      0b      80   \n",
  75 |             "9  1.478198e+09   0131     8      f7      7f      00      00      4c      7f   \n",
  76 |             "\n",
  77 |             "  DATA[6] DATA[7]      AttackType  \n",
  78 |             "0      00      00      DoS Attack  \n",
  79 |             "1      0e      a6  Normal Message  \n",
  80 |             "2      00      00  Normal Message  \n",
  81 |             "3      70      05  Normal Message  \n",
  82 |             "4      00      00  Normal Message  \n",
  83 |             "5      04      88  Normal Message  \n",
  84 |             "6      00      00  Normal Message  \n",
  85 |             "7      d1      13  Normal Message  \n",
  86 |             "8      0c      ed  Normal Message  \n",
  87 |             "9      0d      e7  Normal Message  \n",
  88 |             "(1656947, 12)\n"
  89 |           ]
  90 |         }
  91 |       ]
  92 |     },
  93 |     {
  94 |       "cell_type": "code",
  95 |       "source": [
  96 |         "print(df.dtypes)\n",
  97 |         "df = df.dropna()\n",
  98 |         "def changecolumntype(df):\n",
  99 |         "    for column in df[['CAN ID', 'DATA[0]', 'DATA[1]', 'DATA[2]', 'DATA[3]', 'DATA[4]', 'DATA[5]', 'DATA[6]', 'DATA[7]']]:\n",
 100 |         "        df[column] = df[column].apply(lambda x: int(str(x), base=16))\n",
 101 |         "    return df\n",
 102 |         "\n",
 103 |         "df = changecolumntype(df)\n",
 104 |         "print(df.dtypes)\n",
 105 |         "df.head(10)"
 106 |       ],
 107 |       "metadata": {
 108 |         "colab": {
 109 |           "base_uri": "https://localhost:8080/",
 110 |           "height": 1000
 111 |         },
 112 |         "id": "XZTb7XOpJhQw",
 113 |         "outputId": "2755474a-cd7b-4575-cca0-7b7017da8297"
 114 |       },
 115 |       "execution_count": 3,
 116 |       "outputs": [
 117 |         {
 118 |           "output_type": "stream",
 119 |           "name": "stdout",
 120 |           "text": [
 121 |             "Timestamp     float64\n",
 122 |             "CAN ID         object\n",
 123 |             "Byte            int64\n",
 124 |             "DATA[0]        object\n",
 125 |             "DATA[1]        object\n",
 126 |             "DATA[2]        object\n",
 127 |             "DATA[3]        object\n",
 128 |             "DATA[4]        object\n",
 129 |             "DATA[5]        object\n",
 130 |             "DATA[6]        object\n",
 131 |             "DATA[7]        object\n",
 132 |             "AttackType     object\n",
 133 |             "dtype: object\n",
 134 |             "Timestamp     float64\n",
 135 |             "CAN ID          int64\n",
 136 |             "Byte            int64\n",
 137 |             "DATA[0]         int64\n",
 138 |             "DATA[1]         int64\n",
 139 |             "DATA[2]         int64\n",
 140 |             "DATA[3]         int64\n",
 141 |             "DATA[4]         int64\n",
 142 |             "DATA[5]         int64\n",
 143 |             "DATA[6]         int64\n",
 144 |             "DATA[7]         int64\n",
 145 |             "AttackType     object\n",
 146 |             "dtype: object\n"
 147 |           ]
 148 |         },
 149 |         {
 150 |           "output_type": "execute_result",
 151 |           "data": {
 152 |             "text/html": [
 153 |               "\n",
 154 |               "  <div id=\"df-07fb84f4-1b56-4725-8c79-156e67941e09\">\n",
 155 |               "    <div class=\"colab-df-container\">\n",
 156 |               "      <div>\n",
 157 |               "<style scoped>\n",
 158 |               "    .dataframe tbody tr th:only-of-type {\n",
 159 |               "        vertical-align: middle;\n",
 160 |               "    }\n",
 161 |               "\n",
 162 |               "    .dataframe tbody tr th {\n",
 163 |               "        vertical-align: top;\n",
 164 |               "    }\n",
 165 |               "\n",
 166 |               "    .dataframe thead th {\n",
 167 |               "        text-align: right;\n",
 168 |               "    }\n",
 169 |               "</style>\n",
 170 |               "<table border=\"1\" class=\"dataframe\">\n",
 171 |               "  <thead>\n",
 172 |               "    <tr style=\"text-align: right;\">\n",
 173 |               "      <th></th>\n",
 174 |               "      <th>Timestamp</th>\n",
 175 |               "      <th>CAN ID</th>\n",
 176 |               "      <th>Byte</th>\n",
 177 |               "      <th>DATA[0]</th>\n",
 178 |               "      <th>DATA[1]</th>\n",
 179 |               "      <th>DATA[2]</th>\n",
 180 |               "      <th>DATA[3]</th>\n",
 181 |               "      <th>DATA[4]</th>\n",
 182 |               "      <th>DATA[5]</th>\n",
 183 |               "      <th>DATA[6]</th>\n",
 184 |               "      <th>DATA[7]</th>\n",
 185 |               "      <th>AttackType</th>\n",
 186 |               "    </tr>\n",
 187 |               "  </thead>\n",
 188 |               "  <tbody>\n",
 189 |               "    <tr>\n",
 190 |               "      <th>0</th>\n",
 191 |               "      <td>1.478200e+09</td>\n",
 192 |               "      <td>0</td>\n",
 193 |               "      <td>8</td>\n",
 194 |               "      <td>0</td>\n",
 195 |               "      <td>0</td>\n",
 196 |               "      <td>0</td>\n",
 197 |               "      <td>0</td>\n",
 198 |               "      <td>0</td>\n",
 199 |               "      <td>0</td>\n",
 200 |               "      <td>0</td>\n",
 201 |               "      <td>0</td>\n",
 202 |               "      <td>DoS Attack</td>\n",
 203 |               "    </tr>\n",
 204 |               "    <tr>\n",
 205 |               "      <th>1</th>\n",
 206 |               "      <td>1.478201e+09</td>\n",
 207 |               "      <td>305</td>\n",
 208 |               "      <td>8</td>\n",
 209 |               "      <td>27</td>\n",
 210 |               "      <td>128</td>\n",
 211 |               "      <td>0</td>\n",
 212 |               "      <td>0</td>\n",
 213 |               "      <td>63</td>\n",
 214 |               "      <td>127</td>\n",
 215 |               "      <td>14</td>\n",
 216 |               "      <td>166</td>\n",
 217 |               "      <td>Normal Message</td>\n",
 218 |               "    </tr>\n",
 219 |               "    <tr>\n",
 220 |               "      <th>2</th>\n",
 221 |               "      <td>1.478199e+09</td>\n",
 222 |               "      <td>161</td>\n",
 223 |               "      <td>8</td>\n",
 224 |               "      <td>128</td>\n",
 225 |               "      <td>137</td>\n",
 226 |               "      <td>0</td>\n",
 227 |               "      <td>0</td>\n",
 228 |               "      <td>36</td>\n",
 229 |               "      <td>0</td>\n",
 230 |               "      <td>0</td>\n",
 231 |               "      <td>0</td>\n",
 232 |               "      <td>Normal Message</td>\n",
 233 |               "    </tr>\n",
 234 |               "    <tr>\n",
 235 |               "      <th>3</th>\n",
 236 |               "      <td>1.478200e+09</td>\n",
 237 |               "      <td>608</td>\n",
 238 |               "      <td>8</td>\n",
 239 |               "      <td>24</td>\n",
 240 |               "      <td>33</td>\n",
 241 |               "      <td>34</td>\n",
 242 |               "      <td>48</td>\n",
 243 |               "      <td>8</td>\n",
 244 |               "      <td>143</td>\n",
 245 |               "      <td>112</td>\n",
 246 |               "      <td>5</td>\n",
 247 |               "      <td>Normal Message</td>\n",
 248 |               "    </tr>\n",
 249 |               "    <tr>\n",
 250 |               "      <th>4</th>\n",
 251 |               "      <td>1.478201e+09</td>\n",
 252 |               "      <td>704</td>\n",
 253 |               "      <td>8</td>\n",
 254 |               "      <td>20</td>\n",
 255 |               "      <td>0</td>\n",
 256 |               "      <td>0</td>\n",
 257 |               "      <td>0</td>\n",
 258 |               "      <td>0</td>\n",
 259 |               "      <td>0</td>\n",
 260 |               "      <td>0</td>\n",
 261 |               "      <td>0</td>\n",
 262 |               "      <td>Normal Message</td>\n",
 263 |               "    </tr>\n",
 264 |               "    <tr>\n",
 265 |               "      <th>5</th>\n",
 266 |               "      <td>1.478200e+09</td>\n",
 267 |               "      <td>304</td>\n",
 268 |               "      <td>8</td>\n",
 269 |               "      <td>11</td>\n",
 270 |               "      <td>128</td>\n",
 271 |               "      <td>0</td>\n",
 272 |               "      <td>255</td>\n",
 273 |               "      <td>8</td>\n",
 274 |               "      <td>128</td>\n",
 275 |               "      <td>4</td>\n",
 276 |               "      <td>136</td>\n",
 277 |               "      <td>Normal Message</td>\n",
 278 |               "    </tr>\n",
 279 |               "    <tr>\n",
 280 |               "      <th>6</th>\n",
 281 |               "      <td>1.478200e+09</td>\n",
 282 |               "      <td>880</td>\n",
 283 |               "      <td>8</td>\n",
 284 |               "      <td>0</td>\n",
 285 |               "      <td>32</td>\n",
 286 |               "      <td>0</td>\n",
 287 |               "      <td>0</td>\n",
 288 |               "      <td>0</td>\n",
 289 |               "      <td>0</td>\n",
 290 |               "      <td>0</td>\n",
 291 |               "      <td>0</td>\n",
 292 |               "      <td>Normal Message</td>\n",
 293 |               "    </tr>\n",
 294 |               "    <tr>\n",
 295 |               "      <th>7</th>\n",
 296 |               "      <td>1.478199e+09</td>\n",
 297 |               "      <td>1264</td>\n",
 298 |               "      <td>8</td>\n",
 299 |               "      <td>0</td>\n",
 300 |               "      <td>0</td>\n",
 301 |               "      <td>0</td>\n",
 302 |               "      <td>128</td>\n",
 303 |               "      <td>0</td>\n",
 304 |               "      <td>105</td>\n",
 305 |               "      <td>209</td>\n",
 306 |               "      <td>19</td>\n",
 307 |               "      <td>Normal Message</td>\n",
 308 |               "    </tr>\n",
 309 |               "    <tr>\n",
 310 |               "      <th>8</th>\n",
 311 |               "      <td>1.478199e+09</td>\n",
 312 |               "      <td>304</td>\n",
 313 |               "      <td>8</td>\n",
 314 |               "      <td>5</td>\n",
 315 |               "      <td>128</td>\n",
 316 |               "      <td>0</td>\n",
 317 |               "      <td>255</td>\n",
 318 |               "      <td>11</td>\n",
 319 |               "      <td>128</td>\n",
 320 |               "      <td>12</td>\n",
 321 |               "      <td>237</td>\n",
 322 |               "      <td>Normal Message</td>\n",
 323 |               "    </tr>\n",
 324 |               "    <tr>\n",
 325 |               "      <th>9</th>\n",
 326 |               "      <td>1.478198e+09</td>\n",
 327 |               "      <td>305</td>\n",
 328 |               "      <td>8</td>\n",
 329 |               "      <td>247</td>\n",
 330 |               "      <td>127</td>\n",
 331 |               "      <td>0</td>\n",
 332 |               "      <td>0</td>\n",
 333 |               "      <td>76</td>\n",
 334 |               "      <td>127</td>\n",
 335 |               "      <td>13</td>\n",
 336 |               "      <td>231</td>\n",
 337 |               "      <td>Normal Message</td>\n",
 338 |               "    </tr>\n",
 339 |               "  </tbody>\n",
 340 |               "</table>\n",
 341 |               "</div>\n",
 342 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07fb84f4-1b56-4725-8c79-156e67941e09')\"\n",
 343 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
 344 |               "              style=\"display:none;\">\n",
 345 |               "        \n",
 346 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 347 |               "       width=\"24px\">\n",
 348 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
 349 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
 350 |               "  </svg>\n",
 351 |               "      </button>\n",
 352 |               "      \n",
 353 |               "  <style>\n",
 354 |               "    .colab-df-container {\n",
 355 |               "      display:flex;\n",
 356 |               "      flex-wrap:wrap;\n",
 357 |               "      gap: 12px;\n",
 358 |               "    }\n",
 359 |               "\n",
 360 |               "    .colab-df-convert {\n",
 361 |               "      background-color: #E8F0FE;\n",
 362 |               "      border: none;\n",
 363 |               "      border-radius: 50%;\n",
 364 |               "      cursor: pointer;\n",
 365 |               "      display: none;\n",
 366 |               "      fill: #1967D2;\n",
 367 |               "      height: 32px;\n",
 368 |               "      padding: 0 0 0 0;\n",
 369 |               "      width: 32px;\n",
 370 |               "    }\n",
 371 |               "\n",
 372 |               "    .colab-df-convert:hover {\n",
 373 |               "      background-color: #E2EBFA;\n",
 374 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 375 |               "      fill: #174EA6;\n",
 376 |               "    }\n",
 377 |               "\n",
 378 |               "    [theme=dark] .colab-df-convert {\n",
 379 |               "      background-color: #3B4455;\n",
 380 |               "      fill: #D2E3FC;\n",
 381 |               "    }\n",
 382 |               "\n",
 383 |               "    [theme=dark] .colab-df-convert:hover {\n",
 384 |               "      background-color: #434B5C;\n",
 385 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 386 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 387 |               "      fill: #FFFFFF;\n",
 388 |               "    }\n",
 389 |               "  </style>\n",
 390 |               "\n",
 391 |               "      <script>\n",
 392 |               "        const buttonEl =\n",
 393 |               "          document.querySelector('#df-07fb84f4-1b56-4725-8c79-156e67941e09 button.colab-df-convert');\n",
 394 |               "        buttonEl.style.display =\n",
 395 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 396 |               "\n",
 397 |               "        async function convertToInteractive(key) {\n",
 398 |               "          const element = document.querySelector('#df-07fb84f4-1b56-4725-8c79-156e67941e09');\n",
 399 |               "          const dataTable =\n",
 400 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 401 |               "                                                     [key], {});\n",
 402 |               "          if (!dataTable) return;\n",
 403 |               "\n",
 404 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 405 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 406 |               "            + ' to learn more about interactive tables.';\n",
 407 |               "          element.innerHTML = '';\n",
 408 |               "          dataTable['output_type'] = 'display_data';\n",
 409 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
 410 |               "          const docLink = document.createElement('div');\n",
 411 |               "          docLink.innerHTML = docLinkHtml;\n",
 412 |               "          element.appendChild(docLink);\n",
 413 |               "        }\n",
 414 |               "      </script>\n",
 415 |               "    </div>\n",
 416 |               "  </div>\n",
 417 |               "  "
 418 |             ],
 419 |             "text/plain": [
 420 |               "      Timestamp  CAN ID  Byte  DATA[0]  DATA[1]  DATA[2]  DATA[3]  DATA[4]  \\\n",
 421 |               "0  1.478200e+09       0     8        0        0        0        0        0   \n",
 422 |               "1  1.478201e+09     305     8       27      128        0        0       63   \n",
 423 |               "2  1.478199e+09     161     8      128      137        0        0       36   \n",
 424 |               "3  1.478200e+09     608     8       24       33       34       48        8   \n",
 425 |               "4  1.478201e+09     704     8       20        0        0        0        0   \n",
 426 |               "5  1.478200e+09     304     8       11      128        0      255        8   \n",
 427 |               "6  1.478200e+09     880     8        0       32        0        0        0   \n",
 428 |               "7  1.478199e+09    1264     8        0        0        0      128        0   \n",
 429 |               "8  1.478199e+09     304     8        5      128        0      255       11   \n",
 430 |               "9  1.478198e+09     305     8      247      127        0        0       76   \n",
 431 |               "\n",
 432 |               "   DATA[5]  DATA[6]  DATA[7]      AttackType  \n",
 433 |               "0        0        0        0      DoS Attack  \n",
 434 |               "1      127       14      166  Normal Message  \n",
 435 |               "2        0        0        0  Normal Message  \n",
 436 |               "3      143      112        5  Normal Message  \n",
 437 |               "4        0        0        0  Normal Message  \n",
 438 |               "5      128        4      136  Normal Message  \n",
 439 |               "6        0        0        0  Normal Message  \n",
 440 |               "7      105      209       19  Normal Message  \n",
 441 |               "8      128       12      237  Normal Message  \n",
 442 |               "9      127       13      231  Normal Message  "
 443 |             ]
 444 |           },
 445 |           "metadata": {},
 446 |           "execution_count": 3
 447 |         }
 448 |       ]
 449 |     },
 450 |     {
 451 |       "cell_type": "code",
 452 |       "source": [
 453 |         "df['Message'] = df.iloc[:,3:11].apply(lambda x: ''.join(x.astype(str)), axis = 1)\n",
 454 |         "df.head(10)"
 455 |       ],
 456 |       "metadata": {
 457 |         "colab": {
 458 |           "base_uri": "https://localhost:8080/",
 459 |           "height": 601
 460 |         },
 461 |         "id": "ym4-oGjemqFD",
 462 |         "outputId": "2dc335e5-788b-41ff-d48c-1a2602f67391"
 463 |       },
 464 |       "execution_count": 4,
 465 |       "outputs": [
 466 |         {
 467 |           "output_type": "execute_result",
 468 |           "data": {
 469 |             "text/html": [
 470 |               "\n",
 471 |               "  <div id=\"df-52fd1560-0d33-48d5-9d39-54f9e4f8cfc2\">\n",
 472 |               "    <div class=\"colab-df-container\">\n",
 473 |               "      <div>\n",
 474 |               "<style scoped>\n",
 475 |               "    .dataframe tbody tr th:only-of-type {\n",
 476 |               "        vertical-align: middle;\n",
 477 |               "    }\n",
 478 |               "\n",
 479 |               "    .dataframe tbody tr th {\n",
 480 |               "        vertical-align: top;\n",
 481 |               "    }\n",
 482 |               "\n",
 483 |               "    .dataframe thead th {\n",
 484 |               "        text-align: right;\n",
 485 |               "    }\n",
 486 |               "</style>\n",
 487 |               "<table border=\"1\" class=\"dataframe\">\n",
 488 |               "  <thead>\n",
 489 |               "    <tr style=\"text-align: right;\">\n",
 490 |               "      <th></th>\n",
 491 |               "      <th>Timestamp</th>\n",
 492 |               "      <th>CAN ID</th>\n",
 493 |               "      <th>Byte</th>\n",
 494 |               "      <th>DATA[0]</th>\n",
 495 |               "      <th>DATA[1]</th>\n",
 496 |               "      <th>DATA[2]</th>\n",
 497 |               "      <th>DATA[3]</th>\n",
 498 |               "      <th>DATA[4]</th>\n",
 499 |               "      <th>DATA[5]</th>\n",
 500 |               "      <th>DATA[6]</th>\n",
 501 |               "      <th>DATA[7]</th>\n",
 502 |               "      <th>AttackType</th>\n",
 503 |               "      <th>Message</th>\n",
 504 |               "    </tr>\n",
 505 |               "  </thead>\n",
 506 |               "  <tbody>\n",
 507 |               "    <tr>\n",
 508 |               "      <th>0</th>\n",
 509 |               "      <td>1.478200e+09</td>\n",
 510 |               "      <td>0</td>\n",
 511 |               "      <td>8</td>\n",
 512 |               "      <td>0</td>\n",
 513 |               "      <td>0</td>\n",
 514 |               "      <td>0</td>\n",
 515 |               "      <td>0</td>\n",
 516 |               "      <td>0</td>\n",
 517 |               "      <td>0</td>\n",
 518 |               "      <td>0</td>\n",
 519 |               "      <td>0</td>\n",
 520 |               "      <td>DoS Attack</td>\n",
 521 |               "      <td>00000000</td>\n",
 522 |               "    </tr>\n",
 523 |               "    <tr>\n",
 524 |               "      <th>1</th>\n",
 525 |               "      <td>1.478201e+09</td>\n",
 526 |               "      <td>305</td>\n",
 527 |               "      <td>8</td>\n",
 528 |               "      <td>27</td>\n",
 529 |               "      <td>128</td>\n",
 530 |               "      <td>0</td>\n",
 531 |               "      <td>0</td>\n",
 532 |               "      <td>63</td>\n",
 533 |               "      <td>127</td>\n",
 534 |               "      <td>14</td>\n",
 535 |               "      <td>166</td>\n",
 536 |               "      <td>Normal Message</td>\n",
 537 |               "      <td>27128006312714166</td>\n",
 538 |               "    </tr>\n",
 539 |               "    <tr>\n",
 540 |               "      <th>2</th>\n",
 541 |               "      <td>1.478199e+09</td>\n",
 542 |               "      <td>161</td>\n",
 543 |               "      <td>8</td>\n",
 544 |               "      <td>128</td>\n",
 545 |               "      <td>137</td>\n",
 546 |               "      <td>0</td>\n",
 547 |               "      <td>0</td>\n",
 548 |               "      <td>36</td>\n",
 549 |               "      <td>0</td>\n",
 550 |               "      <td>0</td>\n",
 551 |               "      <td>0</td>\n",
 552 |               "      <td>Normal Message</td>\n",
 553 |               "      <td>1281370036000</td>\n",
 554 |               "    </tr>\n",
 555 |               "    <tr>\n",
 556 |               "      <th>3</th>\n",
 557 |               "      <td>1.478200e+09</td>\n",
 558 |               "      <td>608</td>\n",
 559 |               "      <td>8</td>\n",
 560 |               "      <td>24</td>\n",
 561 |               "      <td>33</td>\n",
 562 |               "      <td>34</td>\n",
 563 |               "      <td>48</td>\n",
 564 |               "      <td>8</td>\n",
 565 |               "      <td>143</td>\n",
 566 |               "      <td>112</td>\n",
 567 |               "      <td>5</td>\n",
 568 |               "      <td>Normal Message</td>\n",
 569 |               "      <td>2433344881431125</td>\n",
 570 |               "    </tr>\n",
 571 |               "    <tr>\n",
 572 |               "      <th>4</th>\n",
 573 |               "      <td>1.478201e+09</td>\n",
 574 |               "      <td>704</td>\n",
 575 |               "      <td>8</td>\n",
 576 |               "      <td>20</td>\n",
 577 |               "      <td>0</td>\n",
 578 |               "      <td>0</td>\n",
 579 |               "      <td>0</td>\n",
 580 |               "      <td>0</td>\n",
 581 |               "      <td>0</td>\n",
 582 |               "      <td>0</td>\n",
 583 |               "      <td>0</td>\n",
 584 |               "      <td>Normal Message</td>\n",
 585 |               "      <td>200000000</td>\n",
 586 |               "    </tr>\n",
 587 |               "    <tr>\n",
 588 |               "      <th>5</th>\n",
 589 |               "      <td>1.478200e+09</td>\n",
 590 |               "      <td>304</td>\n",
 591 |               "      <td>8</td>\n",
 592 |               "      <td>11</td>\n",
 593 |               "      <td>128</td>\n",
 594 |               "      <td>0</td>\n",
 595 |               "      <td>255</td>\n",
 596 |               "      <td>8</td>\n",
 597 |               "      <td>128</td>\n",
 598 |               "      <td>4</td>\n",
 599 |               "      <td>136</td>\n",
 600 |               "      <td>Normal Message</td>\n",
 601 |               "      <td>11128025581284136</td>\n",
 602 |               "    </tr>\n",
 603 |               "    <tr>\n",
 604 |               "      <th>6</th>\n",
 605 |               "      <td>1.478200e+09</td>\n",
 606 |               "      <td>880</td>\n",
 607 |               "      <td>8</td>\n",
 608 |               "      <td>0</td>\n",
 609 |               "      <td>32</td>\n",
 610 |               "      <td>0</td>\n",
 611 |               "      <td>0</td>\n",
 612 |               "      <td>0</td>\n",
 613 |               "      <td>0</td>\n",
 614 |               "      <td>0</td>\n",
 615 |               "      <td>0</td>\n",
 616 |               "      <td>Normal Message</td>\n",
 617 |               "      <td>032000000</td>\n",
 618 |               "    </tr>\n",
 619 |               "    <tr>\n",
 620 |               "      <th>7</th>\n",
 621 |               "      <td>1.478199e+09</td>\n",
 622 |               "      <td>1264</td>\n",
 623 |               "      <td>8</td>\n",
 624 |               "      <td>0</td>\n",
 625 |               "      <td>0</td>\n",
 626 |               "      <td>0</td>\n",
 627 |               "      <td>128</td>\n",
 628 |               "      <td>0</td>\n",
 629 |               "      <td>105</td>\n",
 630 |               "      <td>209</td>\n",
 631 |               "      <td>19</td>\n",
 632 |               "      <td>Normal Message</td>\n",
 633 |               "      <td>000128010520919</td>\n",
 634 |               "    </tr>\n",
 635 |               "    <tr>\n",
 636 |               "      <th>8</th>\n",
 637 |               "      <td>1.478199e+09</td>\n",
 638 |               "      <td>304</td>\n",
 639 |               "      <td>8</td>\n",
 640 |               "      <td>5</td>\n",
 641 |               "      <td>128</td>\n",
 642 |               "      <td>0</td>\n",
 643 |               "      <td>255</td>\n",
 644 |               "      <td>11</td>\n",
 645 |               "      <td>128</td>\n",
 646 |               "      <td>12</td>\n",
 647 |               "      <td>237</td>\n",
 648 |               "      <td>Normal Message</td>\n",
 649 |               "      <td>512802551112812237</td>\n",
 650 |               "    </tr>\n",
 651 |               "    <tr>\n",
 652 |               "      <th>9</th>\n",
 653 |               "      <td>1.478198e+09</td>\n",
 654 |               "      <td>305</td>\n",
 655 |               "      <td>8</td>\n",
 656 |               "      <td>247</td>\n",
 657 |               "      <td>127</td>\n",
 658 |               "      <td>0</td>\n",
 659 |               "      <td>0</td>\n",
 660 |               "      <td>76</td>\n",
 661 |               "      <td>127</td>\n",
 662 |               "      <td>13</td>\n",
 663 |               "      <td>231</td>\n",
 664 |               "      <td>Normal Message</td>\n",
 665 |               "      <td>247127007612713231</td>\n",
 666 |               "    </tr>\n",
 667 |               "  </tbody>\n",
 668 |               "</table>\n",
 669 |               "</div>\n",
 670 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52fd1560-0d33-48d5-9d39-54f9e4f8cfc2')\"\n",
 671 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
 672 |               "              style=\"display:none;\">\n",
 673 |               "        \n",
 674 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
 675 |               "       width=\"24px\">\n",
 676 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
 677 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
 678 |               "  </svg>\n",
 679 |               "      </button>\n",
 680 |               "      \n",
 681 |               "  <style>\n",
 682 |               "    .colab-df-container {\n",
 683 |               "      display:flex;\n",
 684 |               "      flex-wrap:wrap;\n",
 685 |               "      gap: 12px;\n",
 686 |               "    }\n",
 687 |               "\n",
 688 |               "    .colab-df-convert {\n",
 689 |               "      background-color: #E8F0FE;\n",
 690 |               "      border: none;\n",
 691 |               "      border-radius: 50%;\n",
 692 |               "      cursor: pointer;\n",
 693 |               "      display: none;\n",
 694 |               "      fill: #1967D2;\n",
 695 |               "      height: 32px;\n",
 696 |               "      padding: 0 0 0 0;\n",
 697 |               "      width: 32px;\n",
 698 |               "    }\n",
 699 |               "\n",
 700 |               "    .colab-df-convert:hover {\n",
 701 |               "      background-color: #E2EBFA;\n",
 702 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
 703 |               "      fill: #174EA6;\n",
 704 |               "    }\n",
 705 |               "\n",
 706 |               "    [theme=dark] .colab-df-convert {\n",
 707 |               "      background-color: #3B4455;\n",
 708 |               "      fill: #D2E3FC;\n",
 709 |               "    }\n",
 710 |               "\n",
 711 |               "    [theme=dark] .colab-df-convert:hover {\n",
 712 |               "      background-color: #434B5C;\n",
 713 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
 714 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
 715 |               "      fill: #FFFFFF;\n",
 716 |               "    }\n",
 717 |               "  </style>\n",
 718 |               "\n",
 719 |               "      <script>\n",
 720 |               "        const buttonEl =\n",
 721 |               "          document.querySelector('#df-52fd1560-0d33-48d5-9d39-54f9e4f8cfc2 button.colab-df-convert');\n",
 722 |               "        buttonEl.style.display =\n",
 723 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
 724 |               "\n",
 725 |               "        async function convertToInteractive(key) {\n",
 726 |               "          const element = document.querySelector('#df-52fd1560-0d33-48d5-9d39-54f9e4f8cfc2');\n",
 727 |               "          const dataTable =\n",
 728 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
 729 |               "                                                     [key], {});\n",
 730 |               "          if (!dataTable) return;\n",
 731 |               "\n",
 732 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
 733 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
 734 |               "            + ' to learn more about interactive tables.';\n",
 735 |               "          element.innerHTML = '';\n",
 736 |               "          dataTable['output_type'] = 'display_data';\n",
 737 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
 738 |               "          const docLink = document.createElement('div');\n",
 739 |               "          docLink.innerHTML = docLinkHtml;\n",
 740 |               "          element.appendChild(docLink);\n",
 741 |               "        }\n",
 742 |               "      </script>\n",
 743 |               "    </div>\n",
 744 |               "  </div>\n",
 745 |               "  "
 746 |             ],
 747 |             "text/plain": [
 748 |               "      Timestamp  CAN ID  Byte  DATA[0]  DATA[1]  DATA[2]  DATA[3]  DATA[4]  \\\n",
 749 |               "0  1.478200e+09       0     8        0        0        0        0        0   \n",
 750 |               "1  1.478201e+09     305     8       27      128        0        0       63   \n",
 751 |               "2  1.478199e+09     161     8      128      137        0        0       36   \n",
 752 |               "3  1.478200e+09     608     8       24       33       34       48        8   \n",
 753 |               "4  1.478201e+09     704     8       20        0        0        0        0   \n",
 754 |               "5  1.478200e+09     304     8       11      128        0      255        8   \n",
 755 |               "6  1.478200e+09     880     8        0       32        0        0        0   \n",
 756 |               "7  1.478199e+09    1264     8        0        0        0      128        0   \n",
 757 |               "8  1.478199e+09     304     8        5      128        0      255       11   \n",
 758 |               "9  1.478198e+09     305     8      247      127        0        0       76   \n",
 759 |               "\n",
 760 |               "   DATA[5]  DATA[6]  DATA[7]      AttackType             Message  \n",
 761 |               "0        0        0        0      DoS Attack            00000000  \n",
 762 |               "1      127       14      166  Normal Message   27128006312714166  \n",
 763 |               "2        0        0        0  Normal Message       1281370036000  \n",
 764 |               "3      143      112        5  Normal Message    2433344881431125  \n",
 765 |               "4        0        0        0  Normal Message           200000000  \n",
 766 |               "5      128        4      136  Normal Message   11128025581284136  \n",
 767 |               "6        0        0        0  Normal Message           032000000  \n",
 768 |               "7      105      209       19  Normal Message     000128010520919  \n",
 769 |               "8      128       12      237  Normal Message  512802551112812237  \n",
 770 |               "9      127       13      231  Normal Message  247127007612713231  "
 771 |             ]
 772 |           },
 773 |           "metadata": {},
 774 |           "execution_count": 4
 775 |         }
 776 |       ]
 777 |     },
 778 |     {
 779 |       "cell_type": "code",
 780 |       "source": [
 781 |         "#df['Message'] = df['Message'].map(lambda x: int(x))\n",
 782 |         "df['Message'] = df['Message'].astype(float)\n",
 783 |         "df.head(10)"
 784 |       ],
 785 |       "metadata": {
 786 |         "colab": {
 787 |           "base_uri": "https://localhost:8080/",
 788 |           "height": 601
 789 |         },
 790 |         "id": "fUPHSmKPAP6_",
 791 |         "outputId": "4d196f89-62c4-4c91-eb66-cb3a8172af9f"
 792 |       },
 793 |       "execution_count": 5,
 794 |       "outputs": [
 795 |         {
 796 |           "output_type": "execute_result",
 797 |           "data": {
 798 |             "text/html": [
 799 |               "\n",
 800 |               "  <div id=\"df-f763f16d-b817-44a9-ad56-4034883016f0\">\n",
 801 |               "    <div class=\"colab-df-container\">\n",
 802 |               "      <div>\n",
 803 |               "<style scoped>\n",
 804 |               "    .dataframe tbody tr th:only-of-type {\n",
 805 |               "        vertical-align: middle;\n",
 806 |               "    }\n",
 807 |               "\n",
 808 |               "    .dataframe tbody tr th {\n",
 809 |               "        vertical-align: top;\n",
 810 |               "    }\n",
 811 |               "\n",
 812 |               "    .dataframe thead th {\n",
 813 |               "        text-align: right;\n",
 814 |               "    }\n",
 815 |               "</style>\n",
 816 |               "<table border=\"1\" class=\"dataframe\">\n",
 817 |               "  <thead>\n",
 818 |               "    <tr style=\"text-align: right;\">\n",
 819 |               "      <th></th>\n",
 820 |               "      <th>Timestamp</th>\n",
 821 |               "      <th>CAN ID</th>\n",
 822 |               "      <th>Byte</th>\n",
 823 |               "      <th>DATA[0]</th>\n",
 824 |               "      <th>DATA[1]</th>\n",
 825 |               "      <th>DATA[2]</th>\n",
 826 |               "      <th>DATA[3]</th>\n",
 827 |               "      <th>DATA[4]</th>\n",
 828 |               "      <th>DATA[5]</th>\n",
 829 |               "      <th>DATA[6]</th>\n",
 830 |               "      <th>DATA[7]</th>\n",
 831 |               "      <th>AttackType</th>\n",
 832 |               "      <th>Message</th>\n",
 833 |               "    </tr>\n",
 834 |               "  </thead>\n",
 835 |               "  <tbody>\n",
 836 |               "    <tr>\n",
 837 |               "      <th>0</th>\n",
 838 |               "      <td>1.478200e+09</td>\n",
 839 |               "      <td>0</td>\n",
 840 |               "      <td>8</td>\n",
 841 |               "      <td>0</td>\n",
 842 |               "      <td>0</td>\n",
 843 |               "      <td>0</td>\n",
 844 |               "      <td>0</td>\n",
 845 |               "      <td>0</td>\n",
 846 |               "      <td>0</td>\n",
 847 |               "      <td>0</td>\n",
 848 |               "      <td>0</td>\n",
 849 |               "      <td>DoS Attack</td>\n",
 850 |               "      <td>0.000000e+00</td>\n",
 851 |               "    </tr>\n",
 852 |               "    <tr>\n",
 853 |               "      <th>1</th>\n",
 854 |               "      <td>1.478201e+09</td>\n",
 855 |               "      <td>305</td>\n",
 856 |               "      <td>8</td>\n",
 857 |               "      <td>27</td>\n",
 858 |               "      <td>128</td>\n",
 859 |               "      <td>0</td>\n",
 860 |               "      <td>0</td>\n",
 861 |               "      <td>63</td>\n",
 862 |               "      <td>127</td>\n",
 863 |               "      <td>14</td>\n",
 864 |               "      <td>166</td>\n",
 865 |               "      <td>Normal Message</td>\n",
 866 |               "      <td>2.712801e+16</td>\n",
 867 |               "    </tr>\n",
 868 |               "    <tr>\n",
 869 |               "      <th>2</th>\n",
 870 |               "      <td>1.478199e+09</td>\n",
 871 |               "      <td>161</td>\n",
 872 |               "      <td>8</td>\n",
 873 |               "      <td>128</td>\n",
 874 |               "      <td>137</td>\n",
 875 |               "      <td>0</td>\n",
 876 |               "      <td>0</td>\n",
 877 |               "      <td>36</td>\n",
 878 |               "      <td>0</td>\n",
 879 |               "      <td>0</td>\n",
 880 |               "      <td>0</td>\n",
 881 |               "      <td>Normal Message</td>\n",
 882 |               "      <td>1.281370e+12</td>\n",
 883 |               "    </tr>\n",
 884 |               "    <tr>\n",
 885 |               "      <th>3</th>\n",
 886 |               "      <td>1.478200e+09</td>\n",
 887 |               "      <td>608</td>\n",
 888 |               "      <td>8</td>\n",
 889 |               "      <td>24</td>\n",
 890 |               "      <td>33</td>\n",
 891 |               "      <td>34</td>\n",
 892 |               "      <td>48</td>\n",
 893 |               "      <td>8</td>\n",
 894 |               "      <td>143</td>\n",
 895 |               "      <td>112</td>\n",
 896 |               "      <td>5</td>\n",
 897 |               "      <td>Normal Message</td>\n",
 898 |               "      <td>2.433345e+15</td>\n",
 899 |               "    </tr>\n",
 900 |               "    <tr>\n",
 901 |               "      <th>4</th>\n",
 902 |               "      <td>1.478201e+09</td>\n",
 903 |               "      <td>704</td>\n",
 904 |               "      <td>8</td>\n",
 905 |               "      <td>20</td>\n",
 906 |               "      <td>0</td>\n",
 907 |               "      <td>0</td>\n",
 908 |               "      <td>0</td>\n",
 909 |               "      <td>0</td>\n",
 910 |               "      <td>0</td>\n",
 911 |               "      <td>0</td>\n",
 912 |               "      <td>0</td>\n",
 913 |               "      <td>Normal Message</td>\n",
 914 |               "      <td>2.000000e+08</td>\n",
 915 |               "    </tr>\n",
 916 |               "    <tr>\n",
 917 |               "      <th>5</th>\n",
 918 |               "      <td>1.478200e+09</td>\n",
 919 |               "      <td>304</td>\n",
 920 |               "      <td>8</td>\n",
 921 |               "      <td>11</td>\n",
 922 |               "      <td>128</td>\n",
 923 |               "      <td>0</td>\n",
 924 |               "      <td>255</td>\n",
 925 |               "      <td>8</td>\n",
 926 |               "      <td>128</td>\n",
 927 |               "      <td>4</td>\n",
 928 |               "      <td>136</td>\n",
 929 |               "      <td>Normal Message</td>\n",
 930 |               "      <td>1.112803e+16</td>\n",
 931 |               "    </tr>\n",
 932 |               "    <tr>\n",
 933 |               "      <th>6</th>\n",
 934 |               "      <td>1.478200e+09</td>\n",
 935 |               "      <td>880</td>\n",
 936 |               "      <td>8</td>\n",
 937 |               "      <td>0</td>\n",
 938 |               "      <td>32</td>\n",
 939 |               "      <td>0</td>\n",
 940 |               "      <td>0</td>\n",
 941 |               "      <td>0</td>\n",
 942 |               "      <td>0</td>\n",
 943 |               "      <td>0</td>\n",
 944 |               "      <td>0</td>\n",
 945 |               "      <td>Normal Message</td>\n",
 946 |               "      <td>3.200000e+07</td>\n",
 947 |               "    </tr>\n",
 948 |               "    <tr>\n",
 949 |               "      <th>7</th>\n",
 950 |               "      <td>1.478199e+09</td>\n",
 951 |               "      <td>1264</td>\n",
 952 |               "      <td>8</td>\n",
 953 |               "      <td>0</td>\n",
 954 |               "      <td>0</td>\n",
 955 |               "      <td>0</td>\n",
 956 |               "      <td>128</td>\n",
 957 |               "      <td>0</td>\n",
 958 |               "      <td>105</td>\n",
 959 |               "      <td>209</td>\n",
 960 |               "      <td>19</td>\n",
 961 |               "      <td>Normal Message</td>\n",
 962 |               "      <td>1.280105e+11</td>\n",
 963 |               "    </tr>\n",
 964 |               "    <tr>\n",
 965 |               "      <th>8</th>\n",
 966 |               "      <td>1.478199e+09</td>\n",
 967 |               "      <td>304</td>\n",
 968 |               "      <td>8</td>\n",
 969 |               "      <td>5</td>\n",
 970 |               "      <td>128</td>\n",
 971 |               "      <td>0</td>\n",
 972 |               "      <td>255</td>\n",
 973 |               "      <td>11</td>\n",
 974 |               "      <td>128</td>\n",
 975 |               "      <td>12</td>\n",
 976 |               "      <td>237</td>\n",
 977 |               "      <td>Normal Message</td>\n",
 978 |               "      <td>5.128026e+17</td>\n",
 979 |               "    </tr>\n",
 980 |               "    <tr>\n",
 981 |               "      <th>9</th>\n",
 982 |               "      <td>1.478198e+09</td>\n",
 983 |               "      <td>305</td>\n",
 984 |               "      <td>8</td>\n",
 985 |               "      <td>247</td>\n",
 986 |               "      <td>127</td>\n",
 987 |               "      <td>0</td>\n",
 988 |               "      <td>0</td>\n",
 989 |               "      <td>76</td>\n",
 990 |               "      <td>127</td>\n",
 991 |               "      <td>13</td>\n",
 992 |               "      <td>231</td>\n",
 993 |               "      <td>Normal Message</td>\n",
 994 |               "      <td>2.471270e+17</td>\n",
 995 |               "    </tr>\n",
 996 |               "  </tbody>\n",
 997 |               "</table>\n",
 998 |               "</div>\n",
 999 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f763f16d-b817-44a9-ad56-4034883016f0')\"\n",
1000 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
1001 |               "              style=\"display:none;\">\n",
1002 |               "        \n",
1003 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1004 |               "       width=\"24px\">\n",
1005 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1006 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
1007 |               "  </svg>\n",
1008 |               "      </button>\n",
1009 |               "      \n",
1010 |               "  <style>\n",
1011 |               "    .colab-df-container {\n",
1012 |               "      display:flex;\n",
1013 |               "      flex-wrap:wrap;\n",
1014 |               "      gap: 12px;\n",
1015 |               "    }\n",
1016 |               "\n",
1017 |               "    .colab-df-convert {\n",
1018 |               "      background-color: #E8F0FE;\n",
1019 |               "      border: none;\n",
1020 |               "      border-radius: 50%;\n",
1021 |               "      cursor: pointer;\n",
1022 |               "      display: none;\n",
1023 |               "      fill: #1967D2;\n",
1024 |               "      height: 32px;\n",
1025 |               "      padding: 0 0 0 0;\n",
1026 |               "      width: 32px;\n",
1027 |               "    }\n",
1028 |               "\n",
1029 |               "    .colab-df-convert:hover {\n",
1030 |               "      background-color: #E2EBFA;\n",
1031 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1032 |               "      fill: #174EA6;\n",
1033 |               "    }\n",
1034 |               "\n",
1035 |               "    [theme=dark] .colab-df-convert {\n",
1036 |               "      background-color: #3B4455;\n",
1037 |               "      fill: #D2E3FC;\n",
1038 |               "    }\n",
1039 |               "\n",
1040 |               "    [theme=dark] .colab-df-convert:hover {\n",
1041 |               "      background-color: #434B5C;\n",
1042 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1043 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1044 |               "      fill: #FFFFFF;\n",
1045 |               "    }\n",
1046 |               "  </style>\n",
1047 |               "\n",
1048 |               "      <script>\n",
1049 |               "        const buttonEl =\n",
1050 |               "          document.querySelector('#df-f763f16d-b817-44a9-ad56-4034883016f0 button.colab-df-convert');\n",
1051 |               "        buttonEl.style.display =\n",
1052 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1053 |               "\n",
1054 |               "        async function convertToInteractive(key) {\n",
1055 |               "          const element = document.querySelector('#df-f763f16d-b817-44a9-ad56-4034883016f0');\n",
1056 |               "          const dataTable =\n",
1057 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1058 |               "                                                     [key], {});\n",
1059 |               "          if (!dataTable) return;\n",
1060 |               "\n",
1061 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1062 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1063 |               "            + ' to learn more about interactive tables.';\n",
1064 |               "          element.innerHTML = '';\n",
1065 |               "          dataTable['output_type'] = 'display_data';\n",
1066 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1067 |               "          const docLink = document.createElement('div');\n",
1068 |               "          docLink.innerHTML = docLinkHtml;\n",
1069 |               "          element.appendChild(docLink);\n",
1070 |               "        }\n",
1071 |               "      </script>\n",
1072 |               "    </div>\n",
1073 |               "  </div>\n",
1074 |               "  "
1075 |             ],
1076 |             "text/plain": [
1077 |               "      Timestamp  CAN ID  Byte  DATA[0]  DATA[1]  DATA[2]  DATA[3]  DATA[4]  \\\n",
1078 |               "0  1.478200e+09       0     8        0        0        0        0        0   \n",
1079 |               "1  1.478201e+09     305     8       27      128        0        0       63   \n",
1080 |               "2  1.478199e+09     161     8      128      137        0        0       36   \n",
1081 |               "3  1.478200e+09     608     8       24       33       34       48        8   \n",
1082 |               "4  1.478201e+09     704     8       20        0        0        0        0   \n",
1083 |               "5  1.478200e+09     304     8       11      128        0      255        8   \n",
1084 |               "6  1.478200e+09     880     8        0       32        0        0        0   \n",
1085 |               "7  1.478199e+09    1264     8        0        0        0      128        0   \n",
1086 |               "8  1.478199e+09     304     8        5      128        0      255       11   \n",
1087 |               "9  1.478198e+09     305     8      247      127        0        0       76   \n",
1088 |               "\n",
1089 |               "   DATA[5]  DATA[6]  DATA[7]      AttackType       Message  \n",
1090 |               "0        0        0        0      DoS Attack  0.000000e+00  \n",
1091 |               "1      127       14      166  Normal Message  2.712801e+16  \n",
1092 |               "2        0        0        0  Normal Message  1.281370e+12  \n",
1093 |               "3      143      112        5  Normal Message  2.433345e+15  \n",
1094 |               "4        0        0        0  Normal Message  2.000000e+08  \n",
1095 |               "5      128        4      136  Normal Message  1.112803e+16  \n",
1096 |               "6        0        0        0  Normal Message  3.200000e+07  \n",
1097 |               "7      105      209       19  Normal Message  1.280105e+11  \n",
1098 |               "8      128       12      237  Normal Message  5.128026e+17  \n",
1099 |               "9      127       13      231  Normal Message  2.471270e+17  "
1100 |             ]
1101 |           },
1102 |           "metadata": {},
1103 |           "execution_count": 5
1104 |         }
1105 |       ]
1106 |     },
1107 |     {
1108 |       "cell_type": "code",
1109 |       "source": [
1110 |         "import datetime\n",
1111 |         "newdf = df.copy(deep = True)\n",
1112 |         "dateformat = \"%Y-%m-%d %H:%M:%S.%f\"\n",
1113 |         "df['Timestamp'] = df['Timestamp'].apply(lambda x: datetime.datetime.fromtimestamp(float(x)).strftime(dateformat))\n",
1114 |         "print(df.dtypes)\n",
1115 |         "df.head(100)"
1116 |       ],
1117 |       "metadata": {
1118 |         "id": "DMt9KCx9ql_W",
1119 |         "colab": {
1120 |           "base_uri": "https://localhost:8080/",
1121 |           "height": 921
1122 |         },
1123 |         "outputId": "68acd251-e138-489f-8e43-ae6b6632c6af"
1124 |       },
1125 |       "execution_count": 6,
1126 |       "outputs": [
1127 |         {
1128 |           "output_type": "stream",
1129 |           "name": "stdout",
1130 |           "text": [
1131 |             "Timestamp      object\n",
1132 |             "CAN ID          int64\n",
1133 |             "Byte            int64\n",
1134 |             "DATA[0]         int64\n",
1135 |             "DATA[1]         int64\n",
1136 |             "DATA[2]         int64\n",
1137 |             "DATA[3]         int64\n",
1138 |             "DATA[4]         int64\n",
1139 |             "DATA[5]         int64\n",
1140 |             "DATA[6]         int64\n",
1141 |             "DATA[7]         int64\n",
1142 |             "AttackType     object\n",
1143 |             "Message       float64\n",
1144 |             "dtype: object\n"
1145 |           ]
1146 |         },
1147 |         {
1148 |           "output_type": "execute_result",
1149 |           "data": {
1150 |             "text/html": [
1151 |               "\n",
1152 |               "  <div id=\"df-e686d4e7-84f8-48f3-8422-816b98e5394b\">\n",
1153 |               "    <div class=\"colab-df-container\">\n",
1154 |               "      <div>\n",
1155 |               "<style scoped>\n",
1156 |               "    .dataframe tbody tr th:only-of-type {\n",
1157 |               "        vertical-align: middle;\n",
1158 |               "    }\n",
1159 |               "\n",
1160 |               "    .dataframe tbody tr th {\n",
1161 |               "        vertical-align: top;\n",
1162 |               "    }\n",
1163 |               "\n",
1164 |               "    .dataframe thead th {\n",
1165 |               "        text-align: right;\n",
1166 |               "    }\n",
1167 |               "</style>\n",
1168 |               "<table border=\"1\" class=\"dataframe\">\n",
1169 |               "  <thead>\n",
1170 |               "    <tr style=\"text-align: right;\">\n",
1171 |               "      <th></th>\n",
1172 |               "      <th>Timestamp</th>\n",
1173 |               "      <th>CAN ID</th>\n",
1174 |               "      <th>Byte</th>\n",
1175 |               "      <th>DATA[0]</th>\n",
1176 |               "      <th>DATA[1]</th>\n",
1177 |               "      <th>DATA[2]</th>\n",
1178 |               "      <th>DATA[3]</th>\n",
1179 |               "      <th>DATA[4]</th>\n",
1180 |               "      <th>DATA[5]</th>\n",
1181 |               "      <th>DATA[6]</th>\n",
1182 |               "      <th>DATA[7]</th>\n",
1183 |               "      <th>AttackType</th>\n",
1184 |               "      <th>Message</th>\n",
1185 |               "    </tr>\n",
1186 |               "  </thead>\n",
1187 |               "  <tbody>\n",
1188 |               "    <tr>\n",
1189 |               "      <th>0</th>\n",
1190 |               "      <td>2016-11-03 19:08:43.044157</td>\n",
1191 |               "      <td>0</td>\n",
1192 |               "      <td>8</td>\n",
1193 |               "      <td>0</td>\n",
1194 |               "      <td>0</td>\n",
1195 |               "      <td>0</td>\n",
1196 |               "      <td>0</td>\n",
1197 |               "      <td>0</td>\n",
1198 |               "      <td>0</td>\n",
1199 |               "      <td>0</td>\n",
1200 |               "      <td>0</td>\n",
1201 |               "      <td>DoS Attack</td>\n",
1202 |               "      <td>0.000000e+00</td>\n",
1203 |               "    </tr>\n",
1204 |               "    <tr>\n",
1205 |               "      <th>1</th>\n",
1206 |               "      <td>2016-11-03 19:24:35.989254</td>\n",
1207 |               "      <td>305</td>\n",
1208 |               "      <td>8</td>\n",
1209 |               "      <td>27</td>\n",
1210 |               "      <td>128</td>\n",
1211 |               "      <td>0</td>\n",
1212 |               "      <td>0</td>\n",
1213 |               "      <td>63</td>\n",
1214 |               "      <td>127</td>\n",
1215 |               "      <td>14</td>\n",
1216 |               "      <td>166</td>\n",
1217 |               "      <td>Normal Message</td>\n",
1218 |               "      <td>2.712801e+16</td>\n",
1219 |               "    </tr>\n",
1220 |               "    <tr>\n",
1221 |               "      <th>2</th>\n",
1222 |               "      <td>2016-11-03 18:54:13.788681</td>\n",
1223 |               "      <td>161</td>\n",
1224 |               "      <td>8</td>\n",
1225 |               "      <td>128</td>\n",
1226 |               "      <td>137</td>\n",
1227 |               "      <td>0</td>\n",
1228 |               "      <td>0</td>\n",
1229 |               "      <td>36</td>\n",
1230 |               "      <td>0</td>\n",
1231 |               "      <td>0</td>\n",
1232 |               "      <td>0</td>\n",
1233 |               "      <td>Normal Message</td>\n",
1234 |               "      <td>1.281370e+12</td>\n",
1235 |               "    </tr>\n",
1236 |               "    <tr>\n",
1237 |               "      <th>3</th>\n",
1238 |               "      <td>2016-11-03 19:06:50.286119</td>\n",
1239 |               "      <td>608</td>\n",
1240 |               "      <td>8</td>\n",
1241 |               "      <td>24</td>\n",
1242 |               "      <td>33</td>\n",
1243 |               "      <td>34</td>\n",
1244 |               "      <td>48</td>\n",
1245 |               "      <td>8</td>\n",
1246 |               "      <td>143</td>\n",
1247 |               "      <td>112</td>\n",
1248 |               "      <td>5</td>\n",
1249 |               "      <td>Normal Message</td>\n",
1250 |               "      <td>2.433345e+15</td>\n",
1251 |               "    </tr>\n",
1252 |               "    <tr>\n",
1253 |               "      <th>4</th>\n",
1254 |               "      <td>2016-11-03 19:26:04.139714</td>\n",
1255 |               "      <td>704</td>\n",
1256 |               "      <td>8</td>\n",
1257 |               "      <td>20</td>\n",
1258 |               "      <td>0</td>\n",
1259 |               "      <td>0</td>\n",
1260 |               "      <td>0</td>\n",
1261 |               "      <td>0</td>\n",
1262 |               "      <td>0</td>\n",
1263 |               "      <td>0</td>\n",
1264 |               "      <td>0</td>\n",
1265 |               "      <td>Normal Message</td>\n",
1266 |               "      <td>2.000000e+08</td>\n",
1267 |               "    </tr>\n",
1268 |               "    <tr>\n",
1269 |               "      <th>...</th>\n",
1270 |               "      <td>...</td>\n",
1271 |               "      <td>...</td>\n",
1272 |               "      <td>...</td>\n",
1273 |               "      <td>...</td>\n",
1274 |               "      <td>...</td>\n",
1275 |               "      <td>...</td>\n",
1276 |               "      <td>...</td>\n",
1277 |               "      <td>...</td>\n",
1278 |               "      <td>...</td>\n",
1279 |               "      <td>...</td>\n",
1280 |               "      <td>...</td>\n",
1281 |               "      <td>...</td>\n",
1282 |               "      <td>...</td>\n",
1283 |               "    </tr>\n",
1284 |               "    <tr>\n",
1285 |               "      <th>95</th>\n",
1286 |               "      <td>2016-11-03 19:05:13.346416</td>\n",
1287 |               "      <td>0</td>\n",
1288 |               "      <td>8</td>\n",
1289 |               "      <td>0</td>\n",
1290 |               "      <td>0</td>\n",
1291 |               "      <td>0</td>\n",
1292 |               "      <td>0</td>\n",
1293 |               "      <td>0</td>\n",
1294 |               "      <td>0</td>\n",
1295 |               "      <td>0</td>\n",
1296 |               "      <td>0</td>\n",
1297 |               "      <td>DoS Attack</td>\n",
1298 |               "      <td>0.000000e+00</td>\n",
1299 |               "    </tr>\n",
1300 |               "    <tr>\n",
1301 |               "      <th>97</th>\n",
1302 |               "      <td>2016-11-03 19:15:01.146305</td>\n",
1303 |               "      <td>704</td>\n",
1304 |               "      <td>8</td>\n",
1305 |               "      <td>20</td>\n",
1306 |               "      <td>0</td>\n",
1307 |               "      <td>0</td>\n",
1308 |               "      <td>0</td>\n",
1309 |               "      <td>0</td>\n",
1310 |               "      <td>0</td>\n",
1311 |               "      <td>0</td>\n",
1312 |               "      <td>0</td>\n",
1313 |               "      <td>Normal Message</td>\n",
1314 |               "      <td>2.000000e+08</td>\n",
1315 |               "    </tr>\n",
1316 |               "    <tr>\n",
1317 |               "      <th>98</th>\n",
1318 |               "      <td>2016-11-03 18:56:54.761137</td>\n",
1319 |               "      <td>809</td>\n",
1320 |               "      <td>8</td>\n",
1321 |               "      <td>220</td>\n",
1322 |               "      <td>190</td>\n",
1323 |               "      <td>127</td>\n",
1324 |               "      <td>20</td>\n",
1325 |               "      <td>17</td>\n",
1326 |               "      <td>32</td>\n",
1327 |               "      <td>0</td>\n",
1328 |               "      <td>20</td>\n",
1329 |               "      <td>Normal Message</td>\n",
1330 |               "      <td>2.201901e+17</td>\n",
1331 |               "    </tr>\n",
1332 |               "    <tr>\n",
1333 |               "      <th>99</th>\n",
1334 |               "      <td>2016-11-03 18:52:14.511839</td>\n",
1335 |               "      <td>497</td>\n",
1336 |               "      <td>8</td>\n",
1337 |               "      <td>8</td>\n",
1338 |               "      <td>0</td>\n",
1339 |               "      <td>0</td>\n",
1340 |               "      <td>0</td>\n",
1341 |               "      <td>0</td>\n",
1342 |               "      <td>0</td>\n",
1343 |               "      <td>0</td>\n",
1344 |               "      <td>0</td>\n",
1345 |               "      <td>Normal Message</td>\n",
1346 |               "      <td>8.000000e+07</td>\n",
1347 |               "    </tr>\n",
1348 |               "    <tr>\n",
1349 |               "      <th>100</th>\n",
1350 |               "      <td>2016-11-03 19:16:38.790256</td>\n",
1351 |               "      <td>704</td>\n",
1352 |               "      <td>8</td>\n",
1353 |               "      <td>20</td>\n",
1354 |               "      <td>0</td>\n",
1355 |               "      <td>0</td>\n",
1356 |               "      <td>0</td>\n",
1357 |               "      <td>0</td>\n",
1358 |               "      <td>0</td>\n",
1359 |               "      <td>0</td>\n",
1360 |               "      <td>0</td>\n",
1361 |               "      <td>Normal Message</td>\n",
1362 |               "      <td>2.000000e+08</td>\n",
1363 |               "    </tr>\n",
1364 |               "  </tbody>\n",
1365 |               "</table>\n",
1366 |               "<p>100 rows × 13 columns</p>\n",
1367 |               "</div>\n",
1368 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e686d4e7-84f8-48f3-8422-816b98e5394b')\"\n",
1369 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
1370 |               "              style=\"display:none;\">\n",
1371 |               "        \n",
1372 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1373 |               "       width=\"24px\">\n",
1374 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
1375 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
1376 |               "  </svg>\n",
1377 |               "      </button>\n",
1378 |               "      \n",
1379 |               "  <style>\n",
1380 |               "    .colab-df-container {\n",
1381 |               "      display:flex;\n",
1382 |               "      flex-wrap:wrap;\n",
1383 |               "      gap: 12px;\n",
1384 |               "    }\n",
1385 |               "\n",
1386 |               "    .colab-df-convert {\n",
1387 |               "      background-color: #E8F0FE;\n",
1388 |               "      border: none;\n",
1389 |               "      border-radius: 50%;\n",
1390 |               "      cursor: pointer;\n",
1391 |               "      display: none;\n",
1392 |               "      fill: #1967D2;\n",
1393 |               "      height: 32px;\n",
1394 |               "      padding: 0 0 0 0;\n",
1395 |               "      width: 32px;\n",
1396 |               "    }\n",
1397 |               "\n",
1398 |               "    .colab-df-convert:hover {\n",
1399 |               "      background-color: #E2EBFA;\n",
1400 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1401 |               "      fill: #174EA6;\n",
1402 |               "    }\n",
1403 |               "\n",
1404 |               "    [theme=dark] .colab-df-convert {\n",
1405 |               "      background-color: #3B4455;\n",
1406 |               "      fill: #D2E3FC;\n",
1407 |               "    }\n",
1408 |               "\n",
1409 |               "    [theme=dark] .colab-df-convert:hover {\n",
1410 |               "      background-color: #434B5C;\n",
1411 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1412 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1413 |               "      fill: #FFFFFF;\n",
1414 |               "    }\n",
1415 |               "  </style>\n",
1416 |               "\n",
1417 |               "      <script>\n",
1418 |               "        const buttonEl =\n",
1419 |               "          document.querySelector('#df-e686d4e7-84f8-48f3-8422-816b98e5394b button.colab-df-convert');\n",
1420 |               "        buttonEl.style.display =\n",
1421 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1422 |               "\n",
1423 |               "        async function convertToInteractive(key) {\n",
1424 |               "          const element = document.querySelector('#df-e686d4e7-84f8-48f3-8422-816b98e5394b');\n",
1425 |               "          const dataTable =\n",
1426 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1427 |               "                                                     [key], {});\n",
1428 |               "          if (!dataTable) return;\n",
1429 |               "\n",
1430 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
1431 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1432 |               "            + ' to learn more about interactive tables.';\n",
1433 |               "          element.innerHTML = '';\n",
1434 |               "          dataTable['output_type'] = 'display_data';\n",
1435 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
1436 |               "          const docLink = document.createElement('div');\n",
1437 |               "          docLink.innerHTML = docLinkHtml;\n",
1438 |               "          element.appendChild(docLink);\n",
1439 |               "        }\n",
1440 |               "      </script>\n",
1441 |               "    </div>\n",
1442 |               "  </div>\n",
1443 |               "  "
1444 |             ],
1445 |             "text/plain": [
1446 |               "                      Timestamp  CAN ID  Byte  DATA[0]  DATA[1]  DATA[2]  \\\n",
1447 |               "0    2016-11-03 19:08:43.044157       0     8        0        0        0   \n",
1448 |               "1    2016-11-03 19:24:35.989254     305     8       27      128        0   \n",
1449 |               "2    2016-11-03 18:54:13.788681     161     8      128      137        0   \n",
1450 |               "3    2016-11-03 19:06:50.286119     608     8       24       33       34   \n",
1451 |               "4    2016-11-03 19:26:04.139714     704     8       20        0        0   \n",
1452 |               "..                          ...     ...   ...      ...      ...      ...   \n",
1453 |               "95   2016-11-03 19:05:13.346416       0     8        0        0        0   \n",
1454 |               "97   2016-11-03 19:15:01.146305     704     8       20        0        0   \n",
1455 |               "98   2016-11-03 18:56:54.761137     809     8      220      190      127   \n",
1456 |               "99   2016-11-03 18:52:14.511839     497     8        8        0        0   \n",
1457 |               "100  2016-11-03 19:16:38.790256     704     8       20        0        0   \n",
1458 |               "\n",
1459 |               "     DATA[3]  DATA[4]  DATA[5]  DATA[6]  DATA[7]      AttackType       Message  \n",
1460 |               "0          0        0        0        0        0      DoS Attack  0.000000e+00  \n",
1461 |               "1          0       63      127       14      166  Normal Message  2.712801e+16  \n",
1462 |               "2          0       36        0        0        0  Normal Message  1.281370e+12  \n",
1463 |               "3         48        8      143      112        5  Normal Message  2.433345e+15  \n",
1464 |               "4          0        0        0        0        0  Normal Message  2.000000e+08  \n",
1465 |               "..       ...      ...      ...      ...      ...             ...           ...  \n",
1466 |               "95         0        0        0        0        0      DoS Attack  0.000000e+00  \n",
1467 |               "97         0        0        0        0        0  Normal Message  2.000000e+08  \n",
1468 |               "98        20       17       32        0       20  Normal Message  2.201901e+17  \n",
1469 |               "99         0        0        0        0        0  Normal Message  8.000000e+07  \n",
1470 |               "100        0        0        0        0        0  Normal Message  2.000000e+08  \n",
1471 |               "\n",
1472 |               "[100 rows x 13 columns]"
1473 |             ]
1474 |           },
1475 |           "metadata": {},
1476 |           "execution_count": 6
1477 |         }
1478 |       ]
1479 |     },
1480 |     {
1481 |       "cell_type": "code",
1482 |       "source": [
1483 |         "#df = newdf.copy(deep = True)\n",
1484 |         "from sklearn import preprocessing\n",
1485 |         "#print(df['AttackType'].unique())\n",
1486 |         "#print(df['AttackType'].value_counts())\n",
1487 |         "encoder = preprocessing.LabelEncoder()\n",
1488 |         "#df1 = df[['AttackType']].copy()\n",
1489 |         "df['AttackType']= encoder.fit_transform(df['AttackType'].values)\n",
1490 |         "# df = df.drop(['AttackType'], axis = 1)\n",
1491 |         "# df1\n",
1492 |         "#df = pd.concat([df.iloc[:,0:11],df1, df.iloc[:, 11:]], axis=1)\n",
1493 |         "#df = pd.get_dummies(df, columns =['AttackType'], prefix = '', prefix_sep = '')\n",
1494 |         "print(df.head(10))\n",
1495 |         "# print(df['AttackType Encode'])\n",
1496 |         "print(df['AttackType'])\n",
1497 |         "print(df.shape)\n",
1498 |         "#print(df.shape)"
1499 |       ],
1500 |       "metadata": {
1501 |         "colab": {
1502 |           "base_uri": "https://localhost:8080/"
1503 |         },
1504 |         "id": "vEUXBabOBtpQ",
1505 |         "outputId": "076885d8-3a39-446d-96b6-0816f653f48a"
1506 |       },
1507 |       "execution_count": 7,
1508 |       "outputs": [
1509 |         {
1510 |           "output_type": "stream",
1511 |           "name": "stdout",
1512 |           "text": [
1513 |             "                    Timestamp  CAN ID  Byte  DATA[0]  DATA[1]  DATA[2]  \\\n",
1514 |             "0  2016-11-03 19:08:43.044157       0     8        0        0        0   \n",
1515 |             "1  2016-11-03 19:24:35.989254     305     8       27      128        0   \n",
1516 |             "2  2016-11-03 18:54:13.788681     161     8      128      137        0   \n",
1517 |             "3  2016-11-03 19:06:50.286119     608     8       24       33       34   \n",
1518 |             "4  2016-11-03 19:26:04.139714     704     8       20        0        0   \n",
1519 |             "5  2016-11-03 19:03:07.624543     304     8       11      128        0   \n",
1520 |             "6  2016-11-03 19:06:31.658461     880     8        0       32        0   \n",
1521 |             "7  2016-11-03 18:55:47.812754    1264     8        0        0        0   \n",
1522 |             "8  2016-11-03 18:46:48.226079     304     8        5      128        0   \n",
1523 |             "9  2016-11-03 18:40:52.891089     305     8      247      127        0   \n",
1524 |             "\n",
1525 |             "   DATA[3]  DATA[4]  DATA[5]  DATA[6]  DATA[7]  AttackType       Message  \n",
1526 |             "0        0        0        0        0        0           0  0.000000e+00  \n",
1527 |             "1        0       63      127       14      166           3  2.712801e+16  \n",
1528 |             "2        0       36        0        0        0           3  1.281370e+12  \n",
1529 |             "3       48        8      143      112        5           3  2.433345e+15  \n",
1530 |             "4        0        0        0        0        0           3  2.000000e+08  \n",
1531 |             "5      255        8      128        4      136           3  1.112803e+16  \n",
1532 |             "6        0        0        0        0        0           3  3.200000e+07  \n",
1533 |             "7      128        0      105      209       19           3  1.280105e+11  \n",
1534 |             "8      255       11      128       12      237           3  5.128026e+17  \n",
1535 |             "9        0       76      127       13      231           3  2.471270e+17  \n",
1536 |             "0         0\n",
1537 |             "1         3\n",
1538 |             "2         3\n",
1539 |             "3         3\n",
1540 |             "4         3\n",
1541 |             "         ..\n",
1542 |             "462165    3\n",
1543 |             "462166    3\n",
1544 |             "462167    3\n",
1545 |             "462168    3\n",
1546 |             "462169    4\n",
1547 |             "Name: AttackType, Length: 1636855, dtype: int64\n",
1548 |             "(1636855, 13)\n"
1549 |           ]
1550 |         }
1551 |       ]
1552 |     },
1553 |     {
1554 |       "cell_type": "code",
1555 |       "source": [
1556 |         "df.columns"
1557 |       ],
1558 |       "metadata": {
1559 |         "colab": {
1560 |           "base_uri": "https://localhost:8080/"
1561 |         },
1562 |         "id": "jPQUX0V2PPkm",
1563 |         "outputId": "45225c4a-9642-444e-bb72-1ff1c5f3e0cc"
1564 |       },
1565 |       "execution_count": 8,
1566 |       "outputs": [
1567 |         {
1568 |           "output_type": "execute_result",
1569 |           "data": {
1570 |             "text/plain": [
1571 |               "Index(['Timestamp', 'CAN ID', 'Byte', 'DATA[0]', 'DATA[1]', 'DATA[2]',\n",
1572 |               "       'DATA[3]', 'DATA[4]', 'DATA[5]', 'DATA[6]', 'DATA[7]', 'AttackType',\n",
1573 |               "       'Message'],\n",
1574 |               "      dtype='object')"
1575 |             ]
1576 |           },
1577 |           "metadata": {},
1578 |           "execution_count": 8
1579 |         }
1580 |       ]
1581 |     },
1582 |     {
1583 |       "cell_type": "code",
1584 |       "source": [
1585 |         "X = df.iloc[:, np.r_[:,1,3:11]]\n",
1586 |         "#X = df[['CAN ID', 'DATA[0]', 'DATA[1]', 'DATA[2]', 'DATA[3]', 'DATA[4]', 'DATA[5]', 'DATA[6]', 'DATA[7]']]\n",
1587 |         "Y = df[['AttackType']]\n",
1588 |         "X,Y"
1589 |       ],
1590 |       "metadata": {
1591 |         "colab": {
1592 |           "base_uri": "https://localhost:8080/"
1593 |         },
1594 |         "id": "-E_zgBKtbX4C",
1595 |         "outputId": "fe0d954e-9b94-46d9-f3a1-3e8503d67c3d"
1596 |       },
1597 |       "execution_count": 9,
1598 |       "outputs": [
1599 |         {
1600 |           "output_type": "execute_result",
1601 |           "data": {
1602 |             "text/plain": [
1603 |               "(        CAN ID  DATA[0]  DATA[1]  DATA[2]  DATA[3]  DATA[4]  DATA[5]  DATA[6]  \\\n",
1604 |               " 0            0        0        0        0        0        0        0        0   \n",
1605 |               " 1          305       27      128        0        0       63      127       14   \n",
1606 |               " 2          161      128      137        0        0       36        0        0   \n",
1607 |               " 3          608       24       33       34       48        8      143      112   \n",
1608 |               " 4          704       20        0        0        0        0        0        0   \n",
1609 |               " ...        ...      ...      ...      ...      ...      ...      ...      ...   \n",
1610 |               " 462165     809      220      183      126       20       17       32        0   \n",
1611 |               " 462166     305      242      127        0        0       58      127       12   \n",
1612 |               " 462167     305      242      127        0        0       64      127        6   \n",
1613 |               " 462168     704       21        0        0        0        0        0        0   \n",
1614 |               " 462169     790       69       41       36      255       41       36        0   \n",
1615 |               " \n",
1616 |               "         DATA[7]  \n",
1617 |               " 0             0  \n",
1618 |               " 1           166  \n",
1619 |               " 2             0  \n",
1620 |               " 3             5  \n",
1621 |               " 4             0  \n",
1622 |               " ...         ...  \n",
1623 |               " 462165       20  \n",
1624 |               " 462166      131  \n",
1625 |               " 462167       22  \n",
1626 |               " 462168        0  \n",
1627 |               " 462169      255  \n",
1628 |               " \n",
1629 |               " [1636855 rows x 9 columns],         AttackType\n",
1630 |               " 0                0\n",
1631 |               " 1                3\n",
1632 |               " 2                3\n",
1633 |               " 3                3\n",
1634 |               " 4                3\n",
1635 |               " ...            ...\n",
1636 |               " 462165           3\n",
1637 |               " 462166           3\n",
1638 |               " 462167           3\n",
1639 |               " 462168           3\n",
1640 |               " 462169           4\n",
1641 |               " \n",
1642 |               " [1636855 rows x 1 columns])"
1643 |             ]
1644 |           },
1645 |           "metadata": {},
1646 |           "execution_count": 9
1647 |         }
1648 |       ]
1649 |     },
1650 |     {
1651 |       "cell_type": "code",
1652 |       "source": [
1653 |         "from sklearn.model_selection import train_test_split\n",
1654 |         "from sklearn.svm import SVC\n",
1655 |         "from sklearn.pipeline import Pipeline\n",
1656 |         "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 20)"
1657 |       ],
1658 |       "metadata": {
1659 |         "id": "7_qOxOWjbeX8"
1660 |       },
1661 |       "execution_count": 10,
1662 |       "outputs": []
1663 |     },
1664 |     {
1665 |       "cell_type": "code",
1666 |       "source": [
1667 |         "val = encoder.inverse_transform(df['AttackType'])\n",
1668 |         "(unique, counts) = np.unique(val, return_counts=True)\n",
1669 |         "frequencies = np.asarray((unique, counts)).T\n",
1670 |         "print(frequencies)\n",
1671 |         "print(df['AttackType'].value_counts())"
1672 |       ],
1673 |       "metadata": {
1674 |         "colab": {
1675 |           "base_uri": "https://localhost:8080/"
1676 |         },
1677 |         "id": "oUcMhUpnh4Tq",
1678 |         "outputId": "6fe79717-8b48-42ea-b5a8-11fe00f2d568"
1679 |       },
1680 |       "execution_count": 23,
1681 |       "outputs": [
1682 |         {
1683 |           "output_type": "stream",
1684 |           "name": "stdout",
1685 |           "text": [
1686 |             "[['DoS Attack' 58469]\n",
1687 |             " ['Fuzzy Attack' 49258]\n",
1688 |             " ['Gear Spooing Attack' 60016]\n",
1689 |             " ['Normal Message' 1403673]\n",
1690 |             " ['RPM Spoofing Attack' 65439]]\n",
1691 |             "3    1403673\n",
1692 |             "4      65439\n",
1693 |             "2      60016\n",
1694 |             "0      58469\n",
1695 |             "1      49258\n",
1696 |             "Name: AttackType, dtype: int64\n"
1697 |           ]
1698 |         }
1699 |       ]
1700 |     },
1701 |     {
1702 |       "cell_type": "code",
1703 |       "source": [
1704 |         "model =  Pipeline([\n",
1705 |         "    ('svc', SVC(random_state=20))\n",
1706 |         "  ])\n",
1707 |         "model.fit(X_train, Y_train)\n",
1708 |         "pred = model.predict(X_test)\n",
1709 |         "pred"
1710 |       ],
1711 |       "metadata": {
1712 |         "colab": {
1713 |           "base_uri": "https://localhost:8080/"
1714 |         },
1715 |         "id": "NkzbFP_GbjSe",
1716 |         "outputId": "bf0d413b-a09f-49d3-82fb-87ce53b04aae"
1717 |       },
1718 |       "execution_count": null,
1719 |       "outputs": [
1720 |         {
1721 |           "output_type": "stream",
1722 |           "name": "stderr",
1723 |           "text": [
1724 |             "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
1725 |             "  y = column_or_1d(y, warn=True)\n"
1726 |           ]
1727 |         }
1728 |       ]
1729 |     },
1730 |     {
1731 |       "cell_type": "code",
1732 |       "source": [
1733 |         "from sklearn.metrics import confusion_matrix\n",
1734 |         "from sklearn.metrics import classification_report\n",
1735 |         "from sklearn.metrics import accuracy_score\n",
1736 |         "accuracy = accuracy_score(Y_test, pred)\n",
1737 |         "print('accuracy : \\n', accuracy)\n",
1738 |         "matrix = confusion_matrix(Y_test,pred)\n",
1739 |         "print('Confusion matrix : \\n',matrix)\n",
1740 |         "matrix = classification_report(Y_test,pred)\n",
1741 |         "print('Classification report : \\n',matrix)"
1742 |       ],
1743 |       "metadata": {
1744 |         "id": "zCFivudpci-x"
1745 |       },
1746 |       "execution_count": null,
1747 |       "outputs": []
1748 |     },
1749 |     {
1750 |       "cell_type": "code",
1751 |       "source": [
1752 |         "dfC = df['AttackType'].value_counts()\n",
1753 |         "dfcount = pd.DataFrame(dfC, index = ['Normal Message','RPM Spoofing Attack','Gear Spooing Attack','DoS Attack','Fuzzy Attack'])\n",
1754 |         "#dfcount_reset = dfcount.reset_index()\n",
1755 |         "dfcount.columns = ['Injected Messages']\n",
1756 |         "#dfcount_reset.set_index('Attack Type')\n",
1757 |         "#dfcount_reset.dropna()\n",
1758 |         "print(\"\\n\",dfcount)\n",
1759 |         "\n",
1760 |         "#index = ['Normal Message','RPM Spoofing Attack','Gear Spooing Attack','DoS Attack','Fuzzy Attack']\n",
1761 |         "# df2frame = dfDos['AttackType'].value_counts()\n",
1762 |         "# df2frame_count = pd.DataFrame(df2frame)\n",
1763 |         "# df2frame_count_reset = df2frame_count.reset_index()\n",
1764 |         "# df2frame_count_reset.columns = ['No Of Normal Message','No Of Injected Messages']\n",
1765 |         "# print(\"\\n\",df2frame_count_reset)"
1766 |       ],
1767 |       "metadata": {
1768 |         "id": "z-aD9lxNQ2hX"
1769 |       },
1770 |       "execution_count": null,
1771 |       "outputs": []
1772 |     },
1773 |     {
1774 |       "cell_type": "code",
1775 |       "source": [
1776 |         "import matplotlib.pyplot as plt\n",
1777 |         "# dffinal = pd.DataFrame({'mass': [0.330, 4.87 , 5.97],\n",
1778 |         "#                    'radius': [2439.7, 6051.8, 6378.1]},\n",
1779 |         "#                   index=['Mercury', 'Venus', 'Earth'])\n",
1780 |         "#plts = dfcount.plot.bar(x='Attack Type', y='Injected Messages', rot=0, figsize=(12, 8))\n",
1781 |         "plot = dfcount.plot.pie(y='Injected Messages',  figsize=(12, 8))"
1782 |       ],
1783 |       "metadata": {
1784 |         "id": "7Am7edMcou4Y"
1785 |       },
1786 |       "execution_count": null,
1787 |       "outputs": []
1788 |     }
1789 |   ]
1790 | }


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/supplementary_code/README.md:
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1 | # The code in this folder shows an example of the pre-processing of the Car-Hacking dataset.
2 | 


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