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bfc0df0f0df8ff75b0fb2546f48068333723bc7e
cuiyiming-gradproj/Experiment/ExpertFeature/.ipynb_checkpoints/tls_cert_length-checkpoint.ipynb
崔一鸣 bfc0df0f0d 增加中期实验数据,代码,ppt
2019-12-23 01:20:51 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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" 0 1 2 3 4\n",
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],
"source": [
"date = '2019-12-20_21'\n",
"example_label_file = '../../DataSet/result/' + date + '/stream_tag.txt'\n",
"example_label_df = pd.read_table(example_label_file, sep='\\s+', header=None)\n",
"example_label_df[3] = 443\n",
"example_label_df[4].value_counts()\n",
"example_label_df"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [
{
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" ('192.168.50.150', 53379, '119.249.48.222', 443): 'douyin',\n",
" ('192.168.50.150', 53391, '182.254.56.55', 443): 'douyin',\n",
" ('192.168.50.150', 53393, '122.14.43.39', 443): 'douyin',\n",
" ('192.168.50.150', 53409, '122.14.43.39', 443): 'douyin',\n",
" ('192.168.50.150', 53412, '122.14.43.39', 443): 'douyin',\n",
" ('192.168.50.150', 53411, '122.14.43.39', 443): 'douyin',\n",
" ('192.168.50.150', 51623, '27.221.84.217', 443): 'douyin',\n",
" ('192.168.50.150', 53417, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53420, '122.14.43.166', 443): 'douyin',\n",
" ('192.168.50.150', 51663, '110.249.203.244', 443): 'douyin',\n",
" ('192.168.50.150', 53422, '122.14.43.166', 443): 'douyin',\n",
" ('192.168.50.150', 53423, '122.14.43.166', 443): 'douyin',\n",
" ('192.168.50.150', 53426, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53428, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53430, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53431, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53432, '119.249.48.222', 443): 'douyin',\n",
" ('192.168.50.150', 53436, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53435, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 51694, '124.132.134.229', 443): 'douyin',\n",
" ('192.168.50.150', 51703, '27.221.84.212', 443): 'douyin',\n",
" ('192.168.50.150', 51708, '110.249.203.244', 443): 'douyin',\n",
" ('192.168.50.150', 53442, '122.14.43.166', 443): 'douyin',\n",
" ('192.168.50.150', 53444, '122.14.43.166', 443): 'douyin',\n",
" ('192.168.50.150', 51735, '110.249.203.244', 443): 'douyin',\n",
" ('192.168.50.150', 53449, '122.14.43.168', 443): 'douyin',\n",
" ('192.168.50.150', 53451, '122.14.43.168', 443): 'douyin',\n",
" ('192.168.50.150', 53453, '114.113.88.123', 443): 'douyin',\n",
" ('192.168.50.150', 53457, '122.14.43.168', 443): 'douyin',\n",
" ('192.168.50.150', 53461, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53471, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53469, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 51758, '27.221.84.217', 443): 'douyin',\n",
" ('192.168.50.150', 51766, '110.249.203.244', 443): 'douyin',\n",
" ('192.168.50.150', 53476, '122.14.43.168', 443): 'douyin',\n",
" ('192.168.50.150', 53514, '122.14.43.169', 443): 'douyin',\n",
" ('192.168.50.150', 53539, '122.14.43.171', 443): 'douyin',\n",
" ('192.168.50.150', 53540, '122.14.43.169', 443): 'douyin',\n",
" ('192.168.50.150', 53538, '122.14.43.169', 443): 'douyin',\n",
" ('192.168.50.150', 53542, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53544, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53545, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53546, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53547, '114.113.88.125', 443): 'douyin',\n",
" ('192.168.50.150', 53552, '122.14.43.168', 443): 'douyin',\n",
" ('192.168.50.150', 53551, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 51776, '110.249.203.244', 443): 'douyin',\n",
" ('192.168.50.150', 53555, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53557, '122.14.43.171', 443): 'douyin',\n",
" ('192.168.50.150', 51800, '110.249.203.244', 443): 'douyin',\n",
" ('192.168.50.150', 53570, '114.113.88.123', 443): 'douyin',\n",
" ('192.168.50.150', 51831, '27.221.34.223', 443): 'douyin',\n",
" ('192.168.50.150', 53582, '122.14.43.171', 443): 'douyin',\n",
" ('192.168.50.150', 53584, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53583, '122.14.43.171', 443): 'douyin',\n",
" ('192.168.50.150', 53580, '119.57.174.38', 443): 'douyin',\n",
" ('192.168.50.150', 53591, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53592, '122.14.43.171', 443): 'douyin',\n",
" ('192.168.50.150', 53596, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 51853, '110.249.203.244', 443): 'douyin',\n",
" ('192.168.50.150', 51868, '27.221.34.223', 443): 'douyin',\n",
" ('192.168.50.150', 51871, '121.22.232.213', 443): 'douyin',\n",
" ('192.168.50.150', 53599, '122.14.43.42', 443): 'douyin',\n",
" ('192.168.50.150', 53598, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53601, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53603, '122.14.43.42', 443): 'douyin',\n",
" ('192.168.50.150', 53604, '122.14.43.42', 443): 'douyin',\n",
" ('192.168.50.150', 53605, '122.14.43.42', 443): 'douyin',\n",
" ('192.168.50.150', 53606, '119.57.174.36', 443): 'douyin',\n",
" ('192.168.50.150', 53600, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53621, '122.14.43.42', 443): 'douyin',\n",
" ('192.168.50.150', 53618, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53619, '122.14.43.42', 443): 'douyin',\n",
" ('192.168.50.150', 53620, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53617, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53641, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53643, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53644, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53642, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53646, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53645, '119.249.48.236', 443): 'douyin',\n",
" ('192.168.50.150', 53647, '114.113.88.123', 443): 'douyin',\n",
" ('192.168.50.150', 51888, '110.249.203.244', 443): 'douyin',\n",
" ('192.168.50.150', 53650, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53649, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53648, '119.57.174.35', 443): 'douyin',\n",
" ('192.168.50.150', 53651, '114.113.88.123', 443): 'douyin',\n",
" ('192.168.50.150', 53653, '122.14.43.42', 443): 'douyin',\n",
" ('192.168.50.150', 53652, '122.14.43.42', 443): 'douyin',\n",
" ('192.168.50.150', 51912, '110.249.203.245', 443): 'douyin',\n",
" ('192.168.50.150', 53665, '122.14.43.43', 443): 'douyin',\n",
" ('192.168.50.150', 53667, '122.14.43.43', 443): 'douyin',\n",
" ('192.168.50.150', 53668, '119.57.174.35', 443): 'douyin',\n",
" ...}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"example_label = {tuple(example_label_df.iloc[i,0:4].values):example_label_df.iloc[i,4] for i in example_label_df.index}\n",
"example_label"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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}
],
"source": [
"example_json_file = '../datasets/2019-12-21/ssl_stat.txt'\n",
"example_json_f = open(example_json_file, 'r')\n",
"#array_shape = (1771,6)\n",
"result_data = list()\n",
"result_label = list()\n",
"result_key = list()\n",
"i = 0\n",
"for line in example_json_f.readlines():\n",
" example_json = ''\n",
" try:\n",
" example_json = json.loads(line)\n",
" except Exception:\n",
" continue\n",
" #标签\n",
" try:\n",
" flow_key = (example_json['sip'], example_json['sport'], example_json['dip'], example_json['dport'])\n",
" result_label.append(example_label[flow_key])\n",
" result_key.append(flow_key)\n",
" except Exception:\n",
" continue\n",
" #print(example_json)\n",
" san_count = 0\n",
" cert_length = [0,0,0,0]\n",
" if 'san' in example_json:\n",
" san_count = len(example_json['san'].split(';'))\n",
" cert = example_json['Cert']\n",
" cert_count = cert['cert_count']\n",
" if cert_count != 0:\n",
" cert_length = [c['length'] for c in cert['cert_list']]\n",
" for i in range(4 - len(cert_length)):\n",
" cert_length.append(0)\n",
" result = [san_count, cert_count]\n",
" result += cert_length\n",
" #print(len(result))\n",
" result_data.append(result)\n",
" i += 1\n",
"header = ['san_count', 'cert_count', 'cert_length1', 'cert_length2', 'cert_length3','cert_length4']\n",
"result_df = pd.DataFrame(result_data, columns=header)\n",
"result_df['label'] = np.array(result_label)\n",
"keys = set(example_label.keys()).difference(set(result_key))\n",
"example_keys = example_label_df.iloc[:,0:4].values.copy()\n",
"for i,value in enumerate(list(example_keys)):\n",
" #print(tuple(value))\n",
" if tuple(value) in keys:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\python38\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:430: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" warnings.warn(\"Default solver will be changed to 'lbfgs' in 0.22. \"\n",
"c:\\python38\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:467: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" warnings.warn(\"Default multi_class will be changed to 'auto' in\"\n",
"c:\\python38\\lib\\site-packages\\sklearn\\svm\\base.py:189: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
" warnings.warn(\"The default value of gamma will change \"\n",
"c:\\python38\\lib\\site-packages\\sklearn\\ensemble\\forest.py:244: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
" warn(\"The default value of n_estimators will change from \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.8947815019092066, 0.8947815019092066, 0.8947815019092066]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn import tree\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import f1_score,recall_score,precision_score\n",
"import random\n",
"examples = result_df.values.copy()\n",
"#print(examples)\n",
"score_df = pd.DataFrame(np.zeros((5,3)),index = ['LogisticRegression', 'SVM', 'GaussianNB', 'tree', 'RandomForest'], \\\n",
" columns = ['precision', 'recall', 'f1'])\n",
"#def a():\n",
"f1_score_list = list()\n",
"recall_score_list = list()\n",
"precision_score_list = list()\n",
"for i in range(1):\n",
" np.random.shuffle(examples)\n",
" examples_train = examples[:int(len(examples)*0.75)]\n",
" examples_test = examples[int(len(examples)*0.75):]\n",
" x_train = examples_train[:,0:-1]\n",
" y_train = examples_train[:,-1]\n",
" x_test = examples_test[:,0:-1]\n",
" y_test = examples_test[:,-1]\n",
" classifer = LogisticRegression()\n",
" classifer.fit(x_train, y_train)\n",
" y_pred = classifer.predict(x_test)\n",
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
"score_df.loc['LogisticRegression'] = scores\n",
"\n",
"f1_score_list = list()\n",
"recall_score_list = list()\n",
"precision_score_list = list()\n",
"for i in range(1):\n",
" #np.random.shuffle(examples)\n",
" examples_train = examples[:int(len(examples)*0.75)]\n",
" examples_test = examples[int(len(examples)*0.75):]\n",
" x_train = examples_train[:,0:-1]\n",
" y_train = examples_train[:,-1]\n",
" x_test = examples_test[:,0:-1]\n",
" y_test = examples_test[:,-1]\n",
" classifer = SVC()\n",
" classifer.fit(x_train, y_train)\n",
" y_pred = classifer.predict(x_test)\n",
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
"score_df.loc['SVM'] = scores\n",
"\n",
"f1_score_list = list()\n",
"recall_score_list = list()\n",
"precision_score_list = list()\n",
"for i in range(1):\n",
" #np.random.shuffle(examples)\n",
" examples_train = examples[:int(len(examples)*0.75)]\n",
" examples_test = examples[int(len(examples)*0.75):]\n",
" x_train = examples_train[:,0:-1]\n",
" y_train = examples_train[:,-1]\n",
" x_test = examples_test[:,0:-1]\n",
" y_test = examples_test[:,-1]\n",
" classifer = GaussianNB()\n",
" classifer.fit(x_train, y_train)\n",
" y_pred = classifer.predict(x_test)\n",
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
"score_df.loc['GaussianNB'] = scores\n",
"\n",
"f1_score_list = list()\n",
"recall_score_list = list()\n",
"precision_score_list = list()\n",
"for i in range(1):\n",
" #np.random.shuffle(examples)\n",
" examples_train = examples[:int(len(examples)*0.75)]\n",
" examples_test = examples[int(len(examples)*0.75):]\n",
" x_train = examples_train[:,0:-1]\n",
" y_train = examples_train[:,-1]\n",
" x_test = examples_test[:,0:-1]\n",
" y_test = examples_test[:,-1]\n",
" classifer = tree.DecisionTreeClassifier()\n",
" classifer.fit(x_train, y_train)\n",
" y_pred = classifer.predict(x_test)\n",
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
"score_df.loc['tree'] = scores\n",
"\n",
"f1_score_list = list()\n",
"recall_score_list = list()\n",
"precision_score_list = list()\n",
"for i in range(1):\n",
" #np.random.shuffle(examples)\n",
" examples_train = examples[:int(len(examples)*0.75)]\n",
" examples_test = examples[int(len(examples)*0.75):]\n",
" x_train = examples_train[:,0:-1]\n",
" y_train = examples_train[:,-1]\n",
" x_test = examples_test[:,0:-1]\n",
" y_test = examples_test[:,-1]\n",
" classifer = RandomForestClassifier()\n",
" classifer.fit(x_train, y_train)\n",
" y_pred = classifer.predict(x_test)\n",
" f1_score_list.append(f1_score(y_test, y_pred, average='micro'))\n",
" recall_score_list.append(recall_score(y_test, y_pred, average='micro'))\n",
" precision_score_list.append(precision_score(y_test, y_pred, average='micro'))\n",
"scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n",
"score_df.loc['RandomForest'] = scores\n",
"ax = score_df.plot.bar(title='tls')\n",
"fig = ax.get_figure()\n",
"fig.savefig('../figure/ssl.svg')\n",
"print(scores)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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