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dtrizna
/
QuasarNix

Model card Files Community
Dmitrijs Trizna commited on
Commit
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1 Parent(s): 7ccb2b7

onehot vectorizer vocab update

Browse files
Files changed (7) hide show
  1. load_models.ipynb +20 -27
  2. quasarnix_tokenizer_data_full_onehot_adv.pickle +0 -3
  3. quasarnix_tokenizer_data_train_onehot_adv.json +4098 -0
  4. quasarnix_tokenizer_data_train_onehot_adv.pickle +0 -3
  5. quasarnix_tokenizer_data_train_onehot_full.json +4098 -0
  6. quasarnix_tokenizer_data_train_onehot_orig.json +4098 -0
  7. quasarnix_tokenizer_data_train_onehot_orig.pickle +0 -3
load_models.ipynb CHANGED
@@ -2,16 +2,11 @@
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
- "execution_count": 1,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
9
  "import os\n",
10
- "import sys\n",
11
- "ROOT = \"..\"\n",
12
- "sys.path.append(ROOT)\n",
13
- "from src.data_utils import load_data, commands_to_loader\n",
14
- "\n",
15
  "import json\n",
16
  "import pickle\n",
17
  "from time import time\n",
@@ -22,18 +17,20 @@
22
  "import torch\n",
23
  "from torch import nn\n",
24
  "from torch.nn import functional as F\n",
25
- "\n",
26
- "import numpy as np\n",
27
  "from xgboost import XGBClassifier\n",
28
- "\n",
29
- "from src.preprocessors import CommandTokenizer, OneHotCustomVectorizer\n",
30
  "from nltk.tokenize import wordpunct_tokenize\n",
31
- "TOKENIZER = wordpunct_tokenize\n",
32
- "\n",
33
  "from sklearn.metrics import roc_curve, accuracy_score, f1_score\n",
34
  "from sklearn.utils import shuffle\n",
35
- "import matplotlib.pyplot as plt\n",
36
  "\n",
 
 
 
 
 
 
 
 
37
  "SEED = 33\n",
38
  "VOCAB_SIZE = 4096\n",
39
  "EMBEDDED_DIM = 64\n",
@@ -81,14 +78,13 @@
81
  },
82
  {
83
  "cell_type": "code",
84
- "execution_count": 2,
85
  "metadata": {},
86
  "outputs": [],
87
  "source": [
88
- "*_, X_test_baseline_cmds = load_data(ROOT, seed=SEED)\n",
89
- "\n",
90
- "# each set has randomly subsampled 50000 commands from the original malicious commands\n",
91
  "\n",
 
92
  "# orig test\n",
93
  "X_test_malicious_cmds_path = os.path.join(ROOT, \"data\", \"X_test_malicious_cmd_orig.json\")\n",
94
  "with open(X_test_malicious_cmds_path, 'r') as f:\n",
@@ -110,23 +106,20 @@
110
  },
111
  {
112
  "cell_type": "code",
113
- "execution_count": 3,
114
  "metadata": {},
115
  "outputs": [],
116
  "source": [
117
  "# TABULAR MODELS\n",
118
  "\n",
119
- "oh_tokenizer_path_orig = \"./quasarnix_tokenizer_data_train_onehot_orig.pickle\"\n",
120
- "with open(oh_tokenizer_path_orig, 'rb') as f:\n",
121
- " oh_tokenizer_orig = pickle.load(f)\n",
122
  "\n",
123
- "oh_tokenizer_path_adv = \"./quasarnix_tokenizer_data_train_onehot_adv.pickle\"\n",
124
- "with open(oh_tokenizer_path_adv, 'rb') as f:\n",
125
- " oh_tokenizer_adv = pickle.load(f)\n",
126
  "\n",
127
- "oh_tokenizer_path_full = \"./quasarnix_tokenizer_data_full_onehot_adv.pickle\"\n",
128
- "with open(oh_tokenizer_path_full, 'rb') as f:\n",
129
- " oh_tokenizer_full = pickle.load(f)\n",
130
  "\n",
131
  "xgb_model_path_orig = './quasarnix_model_data_train_xgb_orig.xgboost'\n",
132
  "xgb_model_path_adv = './quasarnix_model_data_train_xgb_adv.xgboost'\n",
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 8,
6
  "metadata": {},
7
  "outputs": [],
8
  "source": [
9
  "import os\n",
 
 
 
 
 
10
  "import json\n",
11
  "import pickle\n",
12
  "from time import time\n",
 
17
  "import torch\n",
18
  "from torch import nn\n",
19
  "from torch.nn import functional as F\n",
 
 
20
  "from xgboost import XGBClassifier\n",
21
+ "import numpy as np\n",
 
22
  "from nltk.tokenize import wordpunct_tokenize\n",
 
 
23
  "from sklearn.metrics import roc_curve, accuracy_score, f1_score\n",
24
  "from sklearn.utils import shuffle\n",
 
25
  "\n",
26
+ "import sys\n",
27
+ "ROOT = os.path.join(\"..\", \"..\", \"QuasarNix\")\n",
28
+ "sys.path.append(ROOT)\n",
29
+ "# NOTE: src is from https://github.com/dtrizna/QuasarNix -- temp, will be as pip later\n",
30
+ "from src.data_utils import commands_to_loader, load_nl2bash\n",
31
+ "from src.preprocessors import CommandTokenizer, OneHotCustomVectorizer\n",
32
+ "\n",
33
+ "TOKENIZER = wordpunct_tokenize\n",
34
  "SEED = 33\n",
35
  "VOCAB_SIZE = 4096\n",
36
  "EMBEDDED_DIM = 64\n",
 
78
  },
79
  {
80
  "cell_type": "code",
81
+ "execution_count": null,
82
  "metadata": {},
83
  "outputs": [],
84
  "source": [
85
+ "X_test_baseline_cmds = load_nl2bash()\n",
 
 
86
  "\n",
87
+ "# NOTE: malicious data is from https://huggingface.co/datasets/dtrizna/QuasarNix\n",
88
  "# orig test\n",
89
  "X_test_malicious_cmds_path = os.path.join(ROOT, \"data\", \"X_test_malicious_cmd_orig.json\")\n",
90
  "with open(X_test_malicious_cmds_path, 'r') as f:\n",
 
106
  },
107
  {
108
  "cell_type": "code",
109
+ "execution_count": 10,
110
  "metadata": {},
111
  "outputs": [],
112
  "source": [
113
  "# TABULAR MODELS\n",
114
  "\n",
115
+ "oh_tokenizer_orig = OneHotCustomVectorizer(tokenizer=TOKENIZER, max_features=VOCAB_SIZE)\n",
116
+ "oh_tokenizer_orig.load_vocab(\"quasarnix_tokenizer_data_train_onehot_orig.json\")\n",
 
117
  "\n",
118
+ "oh_tokenizer_adv = OneHotCustomVectorizer(tokenizer=TOKENIZER, max_features=VOCAB_SIZE)\n",
119
+ "oh_tokenizer_adv.load_vocab(\"quasarnix_tokenizer_data_train_onehot_adv.json\")\n",
 
120
  "\n",
121
+ "oh_tokenizer_full = OneHotCustomVectorizer(tokenizer=TOKENIZER, max_features=VOCAB_SIZE)\n",
122
+ "oh_tokenizer_full.load_vocab(\"quasarnix_tokenizer_data_train_onehot_full.json\")\n",
 
123
  "\n",
124
  "xgb_model_path_orig = './quasarnix_model_data_train_xgb_orig.xgboost'\n",
125
  "xgb_model_path_adv = './quasarnix_model_data_train_xgb_adv.xgboost'\n",
quasarnix_tokenizer_data_full_onehot_adv.pickle DELETED
@@ -1,3 +0,0 @@
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- version https://git-lfs.github.com/spec/v1
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- oid sha256:72c3d7c3ee1ae6f59555263b242fe6945fe29829bc8f35b1a71b6c6547daf989
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- size 48152
 
 
 
 
quasarnix_tokenizer_data_train_onehot_adv.json ADDED
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