Hugging Face's logo

lakshyarawat1
/
GuardShield_CNNLSTM_v1

Scikit-learn
English
Model card Files Community
lakshyarawat1 commited on
Commit
09a4743
·
verified ·
1 Parent(s): 9423baf

Upload 2 files

Browse files
Files changed (2) hide show
  1. GuardShield_CNNLSTM_v1.h5 +3 -0
  2. GuardShield_CNNLSTM_v1.ipynb +978 -0
GuardShield_CNNLSTM_v1.h5 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a910a015674cbce9cd3bc9f140ca6f16a1f5a1e31237b56e7408538c4ae19db8
3
+ size 1006128
GuardShield_CNNLSTM_v1.ipynb ADDED
@@ -0,0 +1,978 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# GUARDSHIELD_CNNLSTM \n",
8
+ "# Version 1.0"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "markdown",
13
+ "metadata": {},
14
+ "source": [
15
+ "## Loading Dataset"
16
+ ]
17
+ },
18
+ {
19
+ "cell_type": "code",
20
+ "execution_count": 1,
21
+ "metadata": {},
22
+ "outputs": [
23
+ {
24
+ "name": "stderr",
25
+ "output_type": "stream",
26
+ "text": [
27
+ "c:\\Users\\laksh\\anaconda3\\envs\\tf\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
28
+ " from .autonotebook import tqdm as notebook_tqdm\n",
29
+ "HTTP Error 502 thrown while requesting GET https://huggingface.co/datasets/racdroid/cicds-2017/resolve/main/cleaned_data.csv\n",
30
+ "Retrying in 1s [Retry 1/5].\n"
31
+ ]
32
+ }
33
+ ],
34
+ "source": [
35
+ "import pandas as pd\n",
36
+ "\n",
37
+ "df = pd.read_csv(\"hf://datasets/racdroid/cicds-2017/cleaned_data.csv\", engine='pyarrow')"
38
+ ]
39
+ },
40
+ {
41
+ "cell_type": "markdown",
42
+ "metadata": {},
43
+ "source": [
44
+ "## Dataset Enumeration"
45
+ ]
46
+ },
47
+ {
48
+ "cell_type": "code",
49
+ "execution_count": 2,
50
+ "metadata": {},
51
+ "outputs": [
52
+ {
53
+ "data": {
54
+ "text/html": [
55
+ "<div>\n",
56
+ "<style scoped>\n",
57
+ " .dataframe tbody tr th:only-of-type {\n",
58
+ " vertical-align: middle;\n",
59
+ " }\n",
60
+ "\n",
61
+ " .dataframe tbody tr th {\n",
62
+ " vertical-align: top;\n",
63
+ " }\n",
64
+ "\n",
65
+ " .dataframe thead th {\n",
66
+ " text-align: right;\n",
67
+ " }\n",
68
+ "</style>\n",
69
+ "<table border=\"1\" class=\"dataframe\">\n",
70
+ " <thead>\n",
71
+ " <tr style=\"text-align: right;\">\n",
72
+ " <th></th>\n",
73
+ " <th>Destination Port</th>\n",
74
+ " <th>Flow Duration</th>\n",
75
+ " <th>Total Fwd Packets</th>\n",
76
+ " <th>Total Backward Packets</th>\n",
77
+ " <th>Total Length of Fwd Packets</th>\n",
78
+ " <th>Total Length of Bwd Packets</th>\n",
79
+ " <th>Fwd Packet Length Max</th>\n",
80
+ " <th>Fwd Packet Length Min</th>\n",
81
+ " <th>Fwd Packet Length Mean</th>\n",
82
+ " <th>Fwd Packet Length Std</th>\n",
83
+ " <th>...</th>\n",
84
+ " <th>min_seg_size_forward</th>\n",
85
+ " <th>Active Mean</th>\n",
86
+ " <th>Active Std</th>\n",
87
+ " <th>Active Max</th>\n",
88
+ " <th>Active Min</th>\n",
89
+ " <th>Idle Mean</th>\n",
90
+ " <th>Idle Std</th>\n",
91
+ " <th>Idle Max</th>\n",
92
+ " <th>Idle Min</th>\n",
93
+ " <th>Label</th>\n",
94
+ " </tr>\n",
95
+ " </thead>\n",
96
+ " <tbody>\n",
97
+ " <tr>\n",
98
+ " <th>0</th>\n",
99
+ " <td>49188</td>\n",
100
+ " <td>4</td>\n",
101
+ " <td>2</td>\n",
102
+ " <td>0</td>\n",
103
+ " <td>12</td>\n",
104
+ " <td>0</td>\n",
105
+ " <td>6</td>\n",
106
+ " <td>6</td>\n",
107
+ " <td>6.0</td>\n",
108
+ " <td>0.0</td>\n",
109
+ " <td>...</td>\n",
110
+ " <td>20</td>\n",
111
+ " <td>0.0</td>\n",
112
+ " <td>0.0</td>\n",
113
+ " <td>0</td>\n",
114
+ " <td>0</td>\n",
115
+ " <td>0.0</td>\n",
116
+ " <td>0.0</td>\n",
117
+ " <td>0</td>\n",
118
+ " <td>0</td>\n",
119
+ " <td>BENIGN</td>\n",
120
+ " </tr>\n",
121
+ " <tr>\n",
122
+ " <th>1</th>\n",
123
+ " <td>49188</td>\n",
124
+ " <td>1</td>\n",
125
+ " <td>2</td>\n",
126
+ " <td>0</td>\n",
127
+ " <td>12</td>\n",
128
+ " <td>0</td>\n",
129
+ " <td>6</td>\n",
130
+ " <td>6</td>\n",
131
+ " <td>6.0</td>\n",
132
+ " <td>0.0</td>\n",
133
+ " <td>...</td>\n",
134
+ " <td>20</td>\n",
135
+ " <td>0.0</td>\n",
136
+ " <td>0.0</td>\n",
137
+ " <td>0</td>\n",
138
+ " <td>0</td>\n",
139
+ " <td>0.0</td>\n",
140
+ " <td>0.0</td>\n",
141
+ " <td>0</td>\n",
142
+ " <td>0</td>\n",
143
+ " <td>BENIGN</td>\n",
144
+ " </tr>\n",
145
+ " <tr>\n",
146
+ " <th>2</th>\n",
147
+ " <td>49188</td>\n",
148
+ " <td>1</td>\n",
149
+ " <td>2</td>\n",
150
+ " <td>0</td>\n",
151
+ " <td>12</td>\n",
152
+ " <td>0</td>\n",
153
+ " <td>6</td>\n",
154
+ " <td>6</td>\n",
155
+ " <td>6.0</td>\n",
156
+ " <td>0.0</td>\n",
157
+ " <td>...</td>\n",
158
+ " <td>20</td>\n",
159
+ " <td>0.0</td>\n",
160
+ " <td>0.0</td>\n",
161
+ " <td>0</td>\n",
162
+ " <td>0</td>\n",
163
+ " <td>0.0</td>\n",
164
+ " <td>0.0</td>\n",
165
+ " <td>0</td>\n",
166
+ " <td>0</td>\n",
167
+ " <td>BENIGN</td>\n",
168
+ " </tr>\n",
169
+ " <tr>\n",
170
+ " <th>3</th>\n",
171
+ " <td>49188</td>\n",
172
+ " <td>1</td>\n",
173
+ " <td>2</td>\n",
174
+ " <td>0</td>\n",
175
+ " <td>12</td>\n",
176
+ " <td>0</td>\n",
177
+ " <td>6</td>\n",
178
+ " <td>6</td>\n",
179
+ " <td>6.0</td>\n",
180
+ " <td>0.0</td>\n",
181
+ " <td>...</td>\n",
182
+ " <td>20</td>\n",
183
+ " <td>0.0</td>\n",
184
+ " <td>0.0</td>\n",
185
+ " <td>0</td>\n",
186
+ " <td>0</td>\n",
187
+ " <td>0.0</td>\n",
188
+ " <td>0.0</td>\n",
189
+ " <td>0</td>\n",
190
+ " <td>0</td>\n",
191
+ " <td>BENIGN</td>\n",
192
+ " </tr>\n",
193
+ " <tr>\n",
194
+ " <th>4</th>\n",
195
+ " <td>49486</td>\n",
196
+ " <td>3</td>\n",
197
+ " <td>2</td>\n",
198
+ " <td>0</td>\n",
199
+ " <td>12</td>\n",
200
+ " <td>0</td>\n",
201
+ " <td>6</td>\n",
202
+ " <td>6</td>\n",
203
+ " <td>6.0</td>\n",
204
+ " <td>0.0</td>\n",
205
+ " <td>...</td>\n",
206
+ " <td>20</td>\n",
207
+ " <td>0.0</td>\n",
208
+ " <td>0.0</td>\n",
209
+ " <td>0</td>\n",
210
+ " <td>0</td>\n",
211
+ " <td>0.0</td>\n",
212
+ " <td>0.0</td>\n",
213
+ " <td>0</td>\n",
214
+ " <td>0</td>\n",
215
+ " <td>BENIGN</td>\n",
216
+ " </tr>\n",
217
+ " </tbody>\n",
218
+ "</table>\n",
219
+ "<p>5 rows × 67 columns</p>\n",
220
+ "</div>"
221
+ ],
222
+ "text/plain": [
223
+ " Destination Port Flow Duration Total Fwd Packets \\\n",
224
+ "0 49188 4 2 \n",
225
+ "1 49188 1 2 \n",
226
+ "2 49188 1 2 \n",
227
+ "3 49188 1 2 \n",
228
+ "4 49486 3 2 \n",
229
+ "\n",
230
+ " Total Backward Packets Total Length of Fwd Packets \\\n",
231
+ "0 0 12 \n",
232
+ "1 0 12 \n",
233
+ "2 0 12 \n",
234
+ "3 0 12 \n",
235
+ "4 0 12 \n",
236
+ "\n",
237
+ " Total Length of Bwd Packets Fwd Packet Length Max \\\n",
238
+ "0 0 6 \n",
239
+ "1 0 6 \n",
240
+ "2 0 6 \n",
241
+ "3 0 6 \n",
242
+ "4 0 6 \n",
243
+ "\n",
244
+ " Fwd Packet Length Min Fwd Packet Length Mean Fwd Packet Length Std \\\n",
245
+ "0 6 6.0 0.0 \n",
246
+ "1 6 6.0 0.0 \n",
247
+ "2 6 6.0 0.0 \n",
248
+ "3 6 6.0 0.0 \n",
249
+ "4 6 6.0 0.0 \n",
250
+ "\n",
251
+ " ... min_seg_size_forward Active Mean Active Std Active Max \\\n",
252
+ "0 ... 20 0.0 0.0 0 \n",
253
+ "1 ... 20 0.0 0.0 0 \n",
254
+ "2 ... 20 0.0 0.0 0 \n",
255
+ "3 ... 20 0.0 0.0 0 \n",
256
+ "4 ... 20 0.0 0.0 0 \n",
257
+ "\n",
258
+ " Active Min Idle Mean Idle Std Idle Max Idle Min Label \n",
259
+ "0 0 0.0 0.0 0 0 BENIGN \n",
260
+ "1 0 0.0 0.0 0 0 BENIGN \n",
261
+ "2 0 0.0 0.0 0 0 BENIGN \n",
262
+ "3 0 0.0 0.0 0 0 BENIGN \n",
263
+ "4 0 0.0 0.0 0 0 BENIGN \n",
264
+ "\n",
265
+ "[5 rows x 67 columns]"
266
+ ]
267
+ },
268
+ "execution_count": 2,
269
+ "metadata": {},
270
+ "output_type": "execute_result"
271
+ }
272
+ ],
273
+ "source": [
274
+ "df.head()"
275
+ ]
276
+ },
277
+ {
278
+ "cell_type": "code",
279
+ "execution_count": 3,
280
+ "metadata": {},
281
+ "outputs": [
282
+ {
283
+ "data": {
284
+ "text/plain": [
285
+ "(2827876, 67)"
286
+ ]
287
+ },
288
+ "execution_count": 3,
289
+ "metadata": {},
290
+ "output_type": "execute_result"
291
+ }
292
+ ],
293
+ "source": [
294
+ "df.shape"
295
+ ]
296
+ },
297
+ {
298
+ "cell_type": "code",
299
+ "execution_count": 4,
300
+ "metadata": {},
301
+ "outputs": [
302
+ {
303
+ "data": {
304
+ "text/plain": [
305
+ " Destination Port int64\n",
306
+ " Flow Duration int64\n",
307
+ " Total Fwd Packets int64\n",
308
+ " Total Backward Packets int64\n",
309
+ "Total Length of Fwd Packets int64\n",
310
+ " ... \n",
311
+ "Idle Mean float64\n",
312
+ " Idle Std float64\n",
313
+ " Idle Max int64\n",
314
+ " Idle Min int64\n",
315
+ " Label object\n",
316
+ "Length: 67, dtype: object"
317
+ ]
318
+ },
319
+ "execution_count": 4,
320
+ "metadata": {},
321
+ "output_type": "execute_result"
322
+ }
323
+ ],
324
+ "source": [
325
+ "df.dtypes"
326
+ ]
327
+ },
328
+ {
329
+ "cell_type": "code",
330
+ "execution_count": 5,
331
+ "metadata": {},
332
+ "outputs": [
333
+ {
334
+ "data": {
335
+ "text/html": [
336
+ "<div>\n",
337
+ "<style scoped>\n",
338
+ " .dataframe tbody tr th:only-of-type {\n",
339
+ " vertical-align: middle;\n",
340
+ " }\n",
341
+ "\n",
342
+ " .dataframe tbody tr th {\n",
343
+ " vertical-align: top;\n",
344
+ " }\n",
345
+ "\n",
346
+ " .dataframe thead th {\n",
347
+ " text-align: right;\n",
348
+ " }\n",
349
+ "</style>\n",
350
+ "<table border=\"1\" class=\"dataframe\">\n",
351
+ " <thead>\n",
352
+ " <tr style=\"text-align: right;\">\n",
353
+ " <th></th>\n",
354
+ " <th>Destination Port</th>\n",
355
+ " <th>Flow Duration</th>\n",
356
+ " <th>Total Fwd Packets</th>\n",
357
+ " <th>Total Backward Packets</th>\n",
358
+ " <th>Total Length of Fwd Packets</th>\n",
359
+ " <th>Total Length of Bwd Packets</th>\n",
360
+ " <th>Fwd Packet Length Max</th>\n",
361
+ " <th>Fwd Packet Length Min</th>\n",
362
+ " <th>Fwd Packet Length Mean</th>\n",
363
+ " <th>Fwd Packet Length Std</th>\n",
364
+ " <th>...</th>\n",
365
+ " <th>act_data_pkt_fwd</th>\n",
366
+ " <th>min_seg_size_forward</th>\n",
367
+ " <th>Active Mean</th>\n",
368
+ " <th>Active Std</th>\n",
369
+ " <th>Active Max</th>\n",
370
+ " <th>Active Min</th>\n",
371
+ " <th>Idle Mean</th>\n",
372
+ " <th>Idle Std</th>\n",
373
+ " <th>Idle Max</th>\n",
374
+ " <th>Idle Min</th>\n",
375
+ " </tr>\n",
376
+ " </thead>\n",
377
+ " <tbody>\n",
378
+ " <tr>\n",
379
+ " <th>count</th>\n",
380
+ " <td>2.827876e+06</td>\n",
381
+ " <td>2.827876e+06</td>\n",
382
+ " <td>2.827876e+06</td>\n",
383
+ " <td>2.827876e+06</td>\n",
384
+ " <td>2.827876e+06</td>\n",
385
+ " <td>2.827876e+06</td>\n",
386
+ " <td>2.827876e+06</td>\n",
387
+ " <td>2.827876e+06</td>\n",
388
+ " <td>2.827876e+06</td>\n",
389
+ " <td>2.827876e+06</td>\n",
390
+ " <td>...</td>\n",
391
+ " <td>2.827876e+06</td>\n",
392
+ " <td>2.827876e+06</td>\n",
393
+ " <td>2.827876e+06</td>\n",
394
+ " <td>2.827876e+06</td>\n",
395
+ " <td>2.827876e+06</td>\n",
396
+ " <td>2.827876e+06</td>\n",
397
+ " <td>2.827876e+06</td>\n",
398
+ " <td>2.827876e+06</td>\n",
399
+ " <td>2.827876e+06</td>\n",
400
+ " <td>2.827876e+06</td>\n",
401
+ " </tr>\n",
402
+ " <tr>\n",
403
+ " <th>mean</th>\n",
404
+ " <td>8.061534e+03</td>\n",
405
+ " <td>1.480065e+07</td>\n",
406
+ " <td>9.368972e+00</td>\n",
407
+ " <td>1.040396e+01</td>\n",
408
+ " <td>5.498522e+02</td>\n",
409
+ " <td>1.617903e+04</td>\n",
410
+ " <td>2.078044e+02</td>\n",
411
+ " <td>1.872929e+01</td>\n",
412
+ " <td>5.825628e+01</td>\n",
413
+ " <td>6.897811e+01</td>\n",
414
+ " <td>...</td>\n",
415
+ " <td>5.423519e+00</td>\n",
416
+ " <td>-2.744494e+03</td>\n",
417
+ " <td>8.163400e+04</td>\n",
418
+ " <td>4.117582e+04</td>\n",
419
+ " <td>1.533378e+05</td>\n",
420
+ " <td>5.835492e+04</td>\n",
421
+ " <td>8.324468e+06</td>\n",
422
+ " <td>5.043548e+05</td>\n",
423
+ " <td>8.704568e+06</td>\n",
424
+ " <td>7.928061e+06</td>\n",
425
+ " </tr>\n",
426
+ " <tr>\n",
427
+ " <th>std</th>\n",
428
+ " <td>1.827432e+04</td>\n",
429
+ " <td>3.366750e+07</td>\n",
430
+ " <td>7.500527e+02</td>\n",
431
+ " <td>9.978937e+02</td>\n",
432
+ " <td>9.998639e+03</td>\n",
433
+ " <td>2.264235e+06</td>\n",
434
+ " <td>7.175183e+02</td>\n",
435
+ " <td>6.035533e+01</td>\n",
436
+ " <td>1.861733e+02</td>\n",
437
+ " <td>2.813212e+02</td>\n",
438
+ " <td>...</td>\n",
439
+ " <td>6.367482e+02</td>\n",
440
+ " <td>1.085539e+06</td>\n",
441
+ " <td>6.489234e+05</td>\n",
442
+ " <td>3.935787e+05</td>\n",
443
+ " <td>1.026333e+06</td>\n",
444
+ " <td>5.773818e+05</td>\n",
445
+ " <td>2.364057e+07</td>\n",
446
+ " <td>4.605289e+06</td>\n",
447
+ " <td>2.437766e+07</td>\n",
448
+ " <td>2.337390e+07</td>\n",
449
+ " </tr>\n",
450
+ " <tr>\n",
451
+ " <th>min</th>\n",
452
+ " <td>0.000000e+00</td>\n",
453
+ " <td>-1.300000e+01</td>\n",
454
+ " <td>1.000000e+00</td>\n",
455
+ " <td>0.000000e+00</td>\n",
456
+ " <td>0.000000e+00</td>\n",
457
+ " <td>0.000000e+00</td>\n",
458
+ " <td>0.000000e+00</td>\n",
459
+ " <td>0.000000e+00</td>\n",
460
+ " <td>0.000000e+00</td>\n",
461
+ " <td>0.000000e+00</td>\n",
462
+ " <td>...</td>\n",
463
+ " <td>0.000000e+00</td>\n",
464
+ " <td>-5.368707e+08</td>\n",
465
+ " <td>0.000000e+00</td>\n",
466
+ " <td>0.000000e+00</td>\n",
467
+ " <td>0.000000e+00</td>\n",
468
+ " <td>0.000000e+00</td>\n",
469
+ " <td>0.000000e+00</td>\n",
470
+ " <td>0.000000e+00</td>\n",
471
+ " <td>0.000000e+00</td>\n",
472
+ " <td>0.000000e+00</td>\n",
473
+ " </tr>\n",
474
+ " <tr>\n",
475
+ " <th>25%</th>\n",
476
+ " <td>5.300000e+01</td>\n",
477
+ " <td>1.550000e+02</td>\n",
478
+ " <td>2.000000e+00</td>\n",
479
+ " <td>1.000000e+00</td>\n",
480
+ " <td>1.200000e+01</td>\n",
481
+ " <td>2.000000e+00</td>\n",
482
+ " <td>6.000000e+00</td>\n",
483
+ " <td>0.000000e+00</td>\n",
484
+ " <td>6.000000e+00</td>\n",
485
+ " <td>0.000000e+00</td>\n",
486
+ " <td>...</td>\n",
487
+ " <td>0.000000e+00</td>\n",
488
+ " <td>2.000000e+01</td>\n",
489
+ " <td>0.000000e+00</td>\n",
490
+ " <td>0.000000e+00</td>\n",
491
+ " <td>0.000000e+00</td>\n",
492
+ " <td>0.000000e+00</td>\n",
493
+ " <td>0.000000e+00</td>\n",
494
+ " <td>0.000000e+00</td>\n",
495
+ " <td>0.000000e+00</td>\n",
496
+ " <td>0.000000e+00</td>\n",
497
+ " </tr>\n",
498
+ " <tr>\n",
499
+ " <th>50%</th>\n",
500
+ " <td>8.000000e+01</td>\n",
501
+ " <td>3.133800e+04</td>\n",
502
+ " <td>2.000000e+00</td>\n",
503
+ " <td>2.000000e+00</td>\n",
504
+ " <td>6.200000e+01</td>\n",
505
+ " <td>1.230000e+02</td>\n",
506
+ " <td>3.700000e+01</td>\n",
507
+ " <td>2.000000e+00</td>\n",
508
+ " <td>3.400000e+01</td>\n",
509
+ " <td>0.000000e+00</td>\n",
510
+ " <td>...</td>\n",
511
+ " <td>1.000000e+00</td>\n",
512
+ " <td>2.400000e+01</td>\n",
513
+ " <td>0.000000e+00</td>\n",
514
+ " <td>0.000000e+00</td>\n",
515
+ " <td>0.000000e+00</td>\n",
516
+ " <td>0.000000e+00</td>\n",
517
+ " <td>0.000000e+00</td>\n",
518
+ " <td>0.000000e+00</td>\n",
519
+ " <td>0.000000e+00</td>\n",
520
+ " <td>0.000000e+00</td>\n",
521
+ " </tr>\n",
522
+ " <tr>\n",
523
+ " <th>75%</th>\n",
524
+ " <td>4.430000e+02</td>\n",
525
+ " <td>3.239368e+06</td>\n",
526
+ " <td>5.000000e+00</td>\n",
527
+ " <td>4.000000e+00</td>\n",
528
+ " <td>1.880000e+02</td>\n",
529
+ " <td>4.840000e+02</td>\n",
530
+ " <td>8.100000e+01</td>\n",
531
+ " <td>3.600000e+01</td>\n",
532
+ " <td>5.000000e+01</td>\n",
533
+ " <td>2.616295e+01</td>\n",
534
+ " <td>...</td>\n",
535
+ " <td>2.000000e+00</td>\n",
536
+ " <td>3.200000e+01</td>\n",
537
+ " <td>0.000000e+00</td>\n",
538
+ " <td>0.000000e+00</td>\n",
539
+ " <td>0.000000e+00</td>\n",
540
+ " <td>0.000000e+00</td>\n",
541
+ " <td>0.000000e+00</td>\n",
542
+ " <td>0.000000e+00</td>\n",
543
+ " <td>0.000000e+00</td>\n",
544
+ " <td>0.000000e+00</td>\n",
545
+ " </tr>\n",
546
+ " <tr>\n",
547
+ " <th>max</th>\n",
548
+ " <td>6.553500e+04</td>\n",
549
+ " <td>1.200000e+08</td>\n",
550
+ " <td>2.197590e+05</td>\n",
551
+ " <td>2.919220e+05</td>\n",
552
+ " <td>1.290000e+07</td>\n",
553
+ " <td>6.554530e+08</td>\n",
554
+ " <td>2.482000e+04</td>\n",
555
+ " <td>2.325000e+03</td>\n",
556
+ " <td>5.940857e+03</td>\n",
557
+ " <td>7.125597e+03</td>\n",
558
+ " <td>...</td>\n",
559
+ " <td>2.135570e+05</td>\n",
560
+ " <td>1.380000e+02</td>\n",
561
+ " <td>1.100000e+08</td>\n",
562
+ " <td>7.420000e+07</td>\n",
563
+ " <td>1.100000e+08</td>\n",
564
+ " <td>1.100000e+08</td>\n",
565
+ " <td>1.200000e+08</td>\n",
566
+ " <td>7.690000e+07</td>\n",
567
+ " <td>1.200000e+08</td>\n",
568
+ " <td>1.200000e+08</td>\n",
569
+ " </tr>\n",
570
+ " </tbody>\n",
571
+ "</table>\n",
572
+ "<p>8 rows × 66 columns</p>\n",
573
+ "</div>"
574
+ ],
575
+ "text/plain": [
576
+ " Destination Port Flow Duration Total Fwd Packets \\\n",
577
+ "count 2.827876e+06 2.827876e+06 2.827876e+06 \n",
578
+ "mean 8.061534e+03 1.480065e+07 9.368972e+00 \n",
579
+ "std 1.827432e+04 3.366750e+07 7.500527e+02 \n",
580
+ "min 0.000000e+00 -1.300000e+01 1.000000e+00 \n",
581
+ "25% 5.300000e+01 1.550000e+02 2.000000e+00 \n",
582
+ "50% 8.000000e+01 3.133800e+04 2.000000e+00 \n",
583
+ "75% 4.430000e+02 3.239368e+06 5.000000e+00 \n",
584
+ "max 6.553500e+04 1.200000e+08 2.197590e+05 \n",
585
+ "\n",
586
+ " Total Backward Packets Total Length of Fwd Packets \\\n",
587
+ "count 2.827876e+06 2.827876e+06 \n",
588
+ "mean 1.040396e+01 5.498522e+02 \n",
589
+ "std 9.978937e+02 9.998639e+03 \n",
590
+ "min 0.000000e+00 0.000000e+00 \n",
591
+ "25% 1.000000e+00 1.200000e+01 \n",
592
+ "50% 2.000000e+00 6.200000e+01 \n",
593
+ "75% 4.000000e+00 1.880000e+02 \n",
594
+ "max 2.919220e+05 1.290000e+07 \n",
595
+ "\n",
596
+ " Total Length of Bwd Packets Fwd Packet Length Max \\\n",
597
+ "count 2.827876e+06 2.827876e+06 \n",
598
+ "mean 1.617903e+04 2.078044e+02 \n",
599
+ "std 2.264235e+06 7.175183e+02 \n",
600
+ "min 0.000000e+00 0.000000e+00 \n",
601
+ "25% 2.000000e+00 6.000000e+00 \n",
602
+ "50% 1.230000e+02 3.700000e+01 \n",
603
+ "75% 4.840000e+02 8.100000e+01 \n",
604
+ "max 6.554530e+08 2.482000e+04 \n",
605
+ "\n",
606
+ " Fwd Packet Length Min Fwd Packet Length Mean \\\n",
607
+ "count 2.827876e+06 2.827876e+06 \n",
608
+ "mean 1.872929e+01 5.825628e+01 \n",
609
+ "std 6.035533e+01 1.861733e+02 \n",
610
+ "min 0.000000e+00 0.000000e+00 \n",
611
+ "25% 0.000000e+00 6.000000e+00 \n",
612
+ "50% 2.000000e+00 3.400000e+01 \n",
613
+ "75% 3.600000e+01 5.000000e+01 \n",
614
+ "max 2.325000e+03 5.940857e+03 \n",
615
+ "\n",
616
+ " Fwd Packet Length Std ... act_data_pkt_fwd min_seg_size_forward \\\n",
617
+ "count 2.827876e+06 ... 2.827876e+06 2.827876e+06 \n",
618
+ "mean 6.897811e+01 ... 5.423519e+00 -2.744494e+03 \n",
619
+ "std 2.813212e+02 ... 6.367482e+02 1.085539e+06 \n",
620
+ "min 0.000000e+00 ... 0.000000e+00 -5.368707e+08 \n",
621
+ "25% 0.000000e+00 ... 0.000000e+00 2.000000e+01 \n",
622
+ "50% 0.000000e+00 ... 1.000000e+00 2.400000e+01 \n",
623
+ "75% 2.616295e+01 ... 2.000000e+00 3.200000e+01 \n",
624
+ "max 7.125597e+03 ... 2.135570e+05 1.380000e+02 \n",
625
+ "\n",
626
+ " Active Mean Active Std Active Max Active Min Idle Mean \\\n",
627
+ "count 2.827876e+06 2.827876e+06 2.827876e+06 2.827876e+06 2.827876e+06 \n",
628
+ "mean 8.163400e+04 4.117582e+04 1.533378e+05 5.835492e+04 8.324468e+06 \n",
629
+ "std 6.489234e+05 3.935787e+05 1.026333e+06 5.773818e+05 2.364057e+07 \n",
630
+ "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
631
+ "25% 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
632
+ "50% 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
633
+ "75% 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n",
634
+ "max 1.100000e+08 7.420000e+07 1.100000e+08 1.100000e+08 1.200000e+08 \n",
635
+ "\n",
636
+ " Idle Std Idle Max Idle Min \n",
637
+ "count 2.827876e+06 2.827876e+06 2.827876e+06 \n",
638
+ "mean 5.043548e+05 8.704568e+06 7.928061e+06 \n",
639
+ "std 4.605289e+06 2.437766e+07 2.337390e+07 \n",
640
+ "min 0.000000e+00 0.000000e+00 0.000000e+00 \n",
641
+ "25% 0.000000e+00 0.000000e+00 0.000000e+00 \n",
642
+ "50% 0.000000e+00 0.000000e+00 0.000000e+00 \n",
643
+ "75% 0.000000e+00 0.000000e+00 0.000000e+00 \n",
644
+ "max 7.690000e+07 1.200000e+08 1.200000e+08 \n",
645
+ "\n",
646
+ "[8 rows x 66 columns]"
647
+ ]
648
+ },
649
+ "execution_count": 5,
650
+ "metadata": {},
651
+ "output_type": "execute_result"
652
+ }
653
+ ],
654
+ "source": [
655
+ "df.describe()"
656
+ ]
657
+ },
658
+ {
659
+ "cell_type": "code",
660
+ "execution_count": 6,
661
+ "metadata": {},
662
+ "outputs": [
663
+ {
664
+ "name": "stdout",
665
+ "output_type": "stream",
666
+ "text": [
667
+ "<class 'pandas.core.frame.DataFrame'>\n",
668
+ "RangeIndex: 2827876 entries, 0 to 2827875\n",
669
+ "Data columns (total 67 columns):\n",
670
+ " # Column Dtype \n",
671
+ "--- ------ ----- \n",
672
+ " 0 Destination Port int64 \n",
673
+ " 1 Flow Duration int64 \n",
674
+ " 2 Total Fwd Packets int64 \n",
675
+ " 3 Total Backward Packets int64 \n",
676
+ " 4 Total Length of Fwd Packets int64 \n",
677
+ " 5 Total Length of Bwd Packets int64 \n",
678
+ " 6 Fwd Packet Length Max int64 \n",
679
+ " 7 Fwd Packet Length Min int64 \n",
680
+ " 8 Fwd Packet Length Mean float64\n",
681
+ " 9 Fwd Packet Length Std float64\n",
682
+ " 10 Bwd Packet Length Max int64 \n",
683
+ " 11 Bwd Packet Length Min int64 \n",
684
+ " 12 Bwd Packet Length Mean float64\n",
685
+ " 13 Bwd Packet Length Std float64\n",
686
+ " 14 Flow Bytes/s float64\n",
687
+ " 15 Flow Packets/s float64\n",
688
+ " 16 Flow IAT Mean float64\n",
689
+ " 17 Flow IAT Std float64\n",
690
+ " 18 Flow IAT Max int64 \n",
691
+ " 19 Flow IAT Min int64 \n",
692
+ " 20 Fwd IAT Total int64 \n",
693
+ " 21 Fwd IAT Mean float64\n",
694
+ " 22 Fwd IAT Std float64\n",
695
+ " 23 Fwd IAT Max int64 \n",
696
+ " 24 Fwd IAT Min int64 \n",
697
+ " 25 Bwd IAT Total int64 \n",
698
+ " 26 Bwd IAT Mean float64\n",
699
+ " 27 Bwd IAT Std float64\n",
700
+ " 28 Bwd IAT Max int64 \n",
701
+ " 29 Bwd IAT Min int64 \n",
702
+ " 30 Fwd PSH Flags int64 \n",
703
+ " 31 Fwd Header Length int64 \n",
704
+ " 32 Bwd Header Length int64 \n",
705
+ " 33 Fwd Packets/s float64\n",
706
+ " 34 Bwd Packets/s float64\n",
707
+ " 35 Min Packet Length int64 \n",
708
+ " 36 Max Packet Length int64 \n",
709
+ " 37 Packet Length Mean float64\n",
710
+ " 38 Packet Length Std float64\n",
711
+ " 39 Packet Length Variance float64\n",
712
+ " 40 FIN Flag Count int64 \n",
713
+ " 41 SYN Flag Count int64 \n",
714
+ " 42 PSH Flag Count int64 \n",
715
+ " 43 ACK Flag Count int64 \n",
716
+ " 44 URG Flag Count int64 \n",
717
+ " 45 Down/Up Ratio int64 \n",
718
+ " 46 Average Packet Size float64\n",
719
+ " 47 Avg Fwd Segment Size float64\n",
720
+ " 48 Avg Bwd Segment Size float64\n",
721
+ " 49 Fwd Header Length.1 int64 \n",
722
+ " 50 Subflow Fwd Packets int64 \n",
723
+ " 51 Subflow Fwd Bytes int64 \n",
724
+ " 52 Subflow Bwd Packets int64 \n",
725
+ " 53 Subflow Bwd Bytes int64 \n",
726
+ " 54 Init_Win_bytes_forward int64 \n",
727
+ " 55 Init_Win_bytes_backward int64 \n",
728
+ " 56 act_data_pkt_fwd int64 \n",
729
+ " 57 min_seg_size_forward int64 \n",
730
+ " 58 Active Mean float64\n",
731
+ " 59 Active Std float64\n",
732
+ " 60 Active Max int64 \n",
733
+ " 61 Active Min int64 \n",
734
+ " 62 Idle Mean float64\n",
735
+ " 63 Idle Std float64\n",
736
+ " 64 Idle Max int64 \n",
737
+ " 65 Idle Min int64 \n",
738
+ " 66 Label object \n",
739
+ "dtypes: float64(24), int64(42), object(1)\n",
740
+ "memory usage: 1.4+ GB\n"
741
+ ]
742
+ }
743
+ ],
744
+ "source": [
745
+ "df.info()"
746
+ ]
747
+ },
748
+ {
749
+ "cell_type": "code",
750
+ "execution_count": 7,
751
+ "metadata": {},
752
+ "outputs": [
753
+ {
754
+ "data": {
755
+ "text/plain": [
756
+ "Index([' Destination Port', ' Flow Duration', ' Total Fwd Packets',\n",
757
+ " ' Total Backward Packets', 'Total Length of Fwd Packets',\n",
758
+ " ' Total Length of Bwd Packets', ' Fwd Packet Length Max',\n",
759
+ " ' Fwd Packet Length Min', ' Fwd Packet Length Mean',\n",
760
+ " ' Fwd Packet Length Std', 'Bwd Packet Length Max',\n",
761
+ " ' Bwd Packet Length Min', ' Bwd Packet Length Mean',\n",
762
+ " ' Bwd Packet Length Std', 'Flow Bytes/s', ' Flow Packets/s',\n",
763
+ " ' Flow IAT Mean', ' Flow IAT Std', ' Flow IAT Max', ' Flow IAT Min',\n",
764
+ " 'Fwd IAT Total', ' Fwd IAT Mean', ' Fwd IAT Std', ' Fwd IAT Max',\n",
765
+ " ' Fwd IAT Min', 'Bwd IAT Total', ' Bwd IAT Mean', ' Bwd IAT Std',\n",
766
+ " ' Bwd IAT Max', ' Bwd IAT Min', 'Fwd PSH Flags', ' Fwd Header Length',\n",
767
+ " ' Bwd Header Length', 'Fwd Packets/s', ' Bwd Packets/s',\n",
768
+ " ' Min Packet Length', ' Max Packet Length', ' Packet Length Mean',\n",
769
+ " ' Packet Length Std', ' Packet Length Variance', 'FIN Flag Count',\n",
770
+ " ' SYN Flag Count', ' PSH Flag Count', ' ACK Flag Count',\n",
771
+ " ' URG Flag Count', ' Down/Up Ratio', ' Average Packet Size',\n",
772
+ " ' Avg Fwd Segment Size', ' Avg Bwd Segment Size',\n",
773
+ " ' Fwd Header Length.1', 'Subflow Fwd Packets', ' Subflow Fwd Bytes',\n",
774
+ " ' Subflow Bwd Packets', ' Subflow Bwd Bytes', 'Init_Win_bytes_forward',\n",
775
+ " ' Init_Win_bytes_backward', ' act_data_pkt_fwd',\n",
776
+ " ' min_seg_size_forward', 'Active Mean', ' Active Std', ' Active Max',\n",
777
+ " ' Active Min', 'Idle Mean', ' Idle Std', ' Idle Max', ' Idle Min',\n",
778
+ " ' Label'],\n",
779
+ " dtype='object')"
780
+ ]
781
+ },
782
+ "execution_count": 7,
783
+ "metadata": {},
784
+ "output_type": "execute_result"
785
+ }
786
+ ],
787
+ "source": [
788
+ "df.columns"
789
+ ]
790
+ },
791
+ {
792
+ "cell_type": "markdown",
793
+ "metadata": {},
794
+ "source": [
795
+ "## Data Preprocessing"
796
+ ]
797
+ },
798
+ {
799
+ "cell_type": "code",
800
+ "execution_count": 8,
801
+ "metadata": {},
802
+ "outputs": [
803
+ {
804
+ "name": "stdout",
805
+ "output_type": "stream",
806
+ "text": [
807
+ "X_train_shape: (2262296, 5, 66), Y_train_shape: (2262296,), X_test_shape:(565575, 5, 66), Y_test_shape: (565575,)\n"
808
+ ]
809
+ }
810
+ ],
811
+ "source": [
812
+ "import numpy as np\n",
813
+ "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
814
+ "from sklearn.model_selection import train_test_split\n",
815
+ "\n",
816
+ "# Filling missing numerical values with median\n",
817
+ "df.dropna()\n",
818
+ "\n",
819
+ "\n",
820
+ "# Encoding labels using LabelEncoder for classification\n",
821
+ "label_encoder = LabelEncoder()\n",
822
+ "df['Label'] = label_encoder.fit_transform(df[' Label'])\n",
823
+ "\n",
824
+ "# Dropping irrelevant columns\n",
825
+ "df.drop(columns=[' Label'], inplace=True)\n",
826
+ "\n",
827
+ "\n",
828
+ "# Seperating features and target\n",
829
+ "X = df.drop(columns=['Label']).values\n",
830
+ "Y = df['Label'].values\n",
831
+ "\n",
832
+ "\n",
833
+ "# Normalize feature set using StandardScaler\n",
834
+ "scaler=StandardScaler()\n",
835
+ "X=scaler.fit_transform(X)\n",
836
+ "\n",
837
+ "# Preparing sequences for LSTM\n",
838
+ "def create_sequences(X, Y, time_steps=5):\n",
839
+ " Xs, Ys = [],[]\n",
840
+ " for i in range(len(X) - time_steps):\n",
841
+ " Xs.append(X[i:i + time_steps])\n",
842
+ " Ys.append(Y[i+time_steps])\n",
843
+ " return np.array(Xs), np.array(Ys)\n",
844
+ "\n",
845
+ "time_steps = 5\n",
846
+ "X_seq, Y_seq = create_sequences(X,Y, time_steps)\n",
847
+ "\n",
848
+ "# Splitting data into training and testing sets\n",
849
+ "X_train, X_test, Y_train, Y_test = train_test_split(X_seq, Y_seq, test_size=0.2, random_state=42)\n",
850
+ "\n",
851
+ "# Printing\n",
852
+ "print(f'X_train_shape: {X_train.shape}, Y_train_shape: {Y_train.shape}, X_test_shape:{X_test.shape}, Y_test_shape: {Y_test.shape}')"
853
+ ]
854
+ },
855
+ {
856
+ "cell_type": "markdown",
857
+ "metadata": {},
858
+ "source": [
859
+ "## Model Building"
860
+ ]
861
+ },
862
+ {
863
+ "cell_type": "code",
864
+ "execution_count": 20,
865
+ "metadata": {},
866
+ "outputs": [
867
+ {
868
+ "name": "stdout",
869
+ "output_type": "stream",
870
+ "text": [
871
+ "Epoch 1/20\n",
872
+ "28279/28279 [==============================] - 108s 4ms/step - loss: 0.1723 - accuracy: 0.9611 - val_loss: 0.1224 - val_accuracy: 0.9714\n",
873
+ "Epoch 2/20\n",
874
+ "28279/28279 [==============================] - 109s 4ms/step - loss: 0.1266 - accuracy: 0.9708 - val_loss: 0.1142 - val_accuracy: 0.9729\n",
875
+ "Epoch 3/20\n",
876
+ "28279/28279 [==============================] - 109s 4ms/step - loss: 0.1203 - accuracy: 0.9726 - val_loss: 0.1101 - val_accuracy: 0.9752\n",
877
+ "Epoch 4/20\n",
878
+ "28279/28279 [==============================] - 95s 3ms/step - loss: 0.1164 - accuracy: 0.9736 - val_loss: 0.1076 - val_accuracy: 0.9760\n",
879
+ "Epoch 5/20\n",
880
+ "28279/28279 [==============================] - 103s 4ms/step - loss: 0.1141 - accuracy: 0.9744 - val_loss: 0.1062 - val_accuracy: 0.9762\n",
881
+ "Epoch 6/20\n",
882
+ "28279/28279 [==============================] - 112s 4ms/step - loss: 0.1124 - accuracy: 0.9748 - val_loss: 0.1049 - val_accuracy: 0.9765\n",
883
+ "Epoch 7/20\n",
884
+ "28279/28279 [==============================] - 126s 4ms/step - loss: 0.1108 - accuracy: 0.9754 - val_loss: 0.1038 - val_accuracy: 0.9773\n",
885
+ "Epoch 8/20\n",
886
+ "28279/28279 [==============================] - 128s 5ms/step - loss: 0.1092 - accuracy: 0.9760 - val_loss: 0.1024 - val_accuracy: 0.9781\n",
887
+ "Epoch 9/20\n",
888
+ "28279/28279 [==============================] - 123s 4ms/step - loss: 0.1077 - accuracy: 0.9769 - val_loss: 0.1000 - val_accuracy: 0.9789\n",
889
+ "Epoch 10/20\n",
890
+ "28279/28279 [==============================] - 131s 5ms/step - loss: 0.1055 - accuracy: 0.9781 - val_loss: 0.0983 - val_accuracy: 0.9800\n",
891
+ "Epoch 11/20\n",
892
+ "28279/28279 [==============================] - 131s 5ms/step - loss: 0.1042 - accuracy: 0.9788 - val_loss: 0.0962 - val_accuracy: 0.9814\n",
893
+ "Epoch 12/20\n",
894
+ "28279/28279 [==============================] - 115s 4ms/step - loss: 0.1020 - accuracy: 0.9798 - val_loss: 0.0937 - val_accuracy: 0.9821\n",
895
+ "Epoch 13/20\n",
896
+ "28279/28279 [==============================] - 123s 4ms/step - loss: 0.1010 - accuracy: 0.9800 - val_loss: 0.0944 - val_accuracy: 0.9815\n",
897
+ "Epoch 14/20\n",
898
+ "28279/28279 [==============================] - 128s 5ms/step - loss: 0.1009 - accuracy: 0.9799 - val_loss: 0.0961 - val_accuracy: 0.9809\n",
899
+ "Epoch 15/20\n",
900
+ "28279/28279 [==============================] - 131s 5ms/step - loss: 0.1021 - accuracy: 0.9792 - val_loss: 0.0951 - val_accuracy: 0.9813\n",
901
+ "Epoch 16/20\n",
902
+ "28279/28279 [==============================] - 110s 4ms/step - loss: 0.1002 - accuracy: 0.9801 - val_loss: 0.0928 - val_accuracy: 0.9826\n",
903
+ "Epoch 17/20\n",
904
+ "28279/28279 [==============================] - 118s 4ms/step - loss: 0.0992 - accuracy: 0.9804 - val_loss: 0.0909 - val_accuracy: 0.9826\n",
905
+ "Epoch 18/20\n",
906
+ "28279/28279 [==============================] - 116s 4ms/step - loss: 0.0980 - accuracy: 0.9808 - val_loss: 0.0907 - val_accuracy: 0.9827\n",
907
+ "Epoch 19/20\n",
908
+ "28279/28279 [==============================] - 112s 4ms/step - loss: 0.0975 - accuracy: 0.9808 - val_loss: 0.0910 - val_accuracy: 0.9828\n",
909
+ "Epoch 20/20\n",
910
+ "28279/28279 [==============================] - 140s 5ms/step - loss: 0.0980 - accuracy: 0.9804 - val_loss: 0.0898 - val_accuracy: 0.9827\n",
911
+ "17675/17675 [==============================] - 34s 2ms/step - loss: 0.9803 - accuracy: 0.9270\n",
912
+ "Test Loss : 0.9802790284156799, Test Accuracy: 0.9269522428512573\n"
913
+ ]
914
+ }
915
+ ],
916
+ "source": [
917
+ "from tensorflow.keras.models import Sequential\n",
918
+ "from tensorflow.keras.layers import Conv1D, MaxPooling1D, LSTM, Dropout, Dense\n",
919
+ "from tensorflow.keras.optimizers import Adam\n",
920
+ "from tensorflow.keras.utils import to_categorical\n",
921
+ "import tensorflow as tf\n",
922
+ "\n",
923
+ "Y_train = to_categorical(Y_train, num_classes=15)\n",
924
+ "Y_test = to_categorical(Y_test, num_classes=15)\n",
925
+ "\n",
926
+ "\n",
927
+ "model=Sequential() \n",
928
+ "\n",
929
+ "# CNN Layer\n",
930
+ "model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))\n",
931
+ "model.add(MaxPooling1D(pool_size=2))\n",
932
+ "model.add(Dropout(0.3))\n",
933
+ "\n",
934
+ "# LSTM Layer\n",
935
+ "model.add(LSTM(100, return_sequences=False))\n",
936
+ "model.add(Dropout(0.3))\n",
937
+ "model.add(Dense(15, activation='sigmoid'))\n",
938
+ "\n",
939
+ "model.compile(optimizer=Adam(learning_rate=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])\n",
940
+ "\n",
941
+ "history=model.fit(X_train,Y_train, epochs=20, batch_size=64, validation_split=0.2)\n",
942
+ "\n",
943
+ "loss,accuracy = model.evaluate(X_test, Y_test)\n",
944
+ "print(f'Test Loss : {loss}, Test Accuracy: {accuracy}')"
945
+ ]
946
+ },
947
+ {
948
+ "cell_type": "code",
949
+ "execution_count": 21,
950
+ "metadata": {},
951
+ "outputs": [],
952
+ "source": [
953
+ "model.save('GuardShield_CNNLSTM_v1.h5')"
954
+ ]
955
+ }
956
+ ],
957
+ "metadata": {
958
+ "kernelspec": {
959
+ "display_name": "base",
960
+ "language": "python",
961
+ "name": "python3"
962
+ },
963
+ "language_info": {
964
+ "codemirror_mode": {
965
+ "name": "ipython",
966
+ "version": 3
967
+ },
968
+ "file_extension": ".py",
969
+ "mimetype": "text/x-python",
970
+ "name": "python",
971
+ "nbconvert_exporter": "python",
972
+ "pygments_lexer": "ipython3",
973
+ "version": "3.10.13"
974
+ }
975
+ },
976
+ "nbformat": 4,
977
+ "nbformat_minor": 2
978
+ }