diff options
| author | 崔一鸣 <[email protected]> | 2019-12-23 01:20:51 +0800 |
|---|---|---|
| committer | 崔一鸣 <[email protected]> | 2019-12-23 01:20:51 +0800 |
| commit | bfc0df0f0df8ff75b0fb2546f48068333723bc7e (patch) | |
| tree | 79444e8917483a25ccd5b110325915fa67b1566a /Experiment/ExpertFeature | |
| parent | 5508ddeca08a5201451da9e3bb8ff61f1d0940fb (diff) | |
增加中期实验数据,代码,ppt
Diffstat (limited to 'Experiment/ExpertFeature')
| -rw-r--r-- | Experiment/ExpertFeature/.ipynb_checkpoints/tls_cert_length-checkpoint.ipynb | 2611 | ||||
| -rw-r--r-- | Experiment/ExpertFeature/tls_cert_length.ipynb | 542 |
2 files changed, 3153 insertions, 0 deletions
diff --git a/Experiment/ExpertFeature/.ipynb_checkpoints/tls_cert_length-checkpoint.ipynb b/Experiment/ExpertFeature/.ipynb_checkpoints/tls_cert_length-checkpoint.ipynb new file mode 100644 index 0000000..0dc2652 --- /dev/null +++ b/Experiment/ExpertFeature/.ipynb_checkpoints/tls_cert_length-checkpoint.ipynb @@ -0,0 +1,2611 @@ +{ + "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": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>0</th>\n", + " <th>1</th>\n", + " <th>2</th>\n", + " <th>3</th>\n", + " <th>4</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>192.168.50.150</td>\n", + " <td>61251</td>\n", + " <td>119.57.174.36</td>\n", + " <td>443</td>\n", + " <td>douyin</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>192.168.50.150</td>\n", + " <td>61253</td>\n", + " <td>119.57.174.36</td>\n", + " <td>443</td>\n", + " <td>douyin</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>192.168.50.150</td>\n", + " <td>61254</td>\n", + " <td>122.14.43.165</td>\n", + " <td>443</td>\n", + " <td>douyin</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>192.168.50.150</td>\n", + " <td>61258</td>\n", + " <td>119.57.174.38</td>\n", + " <td>443</td>\n", + " <td>douyin</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>192.168.50.150</td>\n", + " <td>61259</td>\n", + " <td>119.57.174.38</td>\n", + " <td>443</td>\n", + " <td>douyin</td>\n", + " </tr>\n", + " <tr>\n", + " <th>...</th>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " <td>...</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10535</th>\n", + " <td>192.168.50.150</td>\n", + " <td>52563</td>\n", + " <td>182.61.128.198</td>\n", + " <td>443</td>\n", + " <td>zhihu</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10536</th>\n", + " <td>192.168.50.150</td>\n", + " <td>52561</td>\n", + " <td>182.61.128.198</td>\n", + " <td>443</td>\n", + " <td>zhihu</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10537</th>\n", + " <td>192.168.50.150</td>\n", + " <td>52562</td>\n", + " <td>182.61.128.198</td>\n", + " <td>443</td>\n", + " <td>zhihu</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10538</th>\n", + " <td>192.168.50.150</td>\n", + " <td>52568</td>\n", + " <td>121.29.54.234</td>\n", + " <td>443</td>\n", + " <td>zhihu</td>\n", + " </tr>\n", + " <tr>\n", + " <th>10539</th>\n", + " <td>192.168.50.150</td>\n", + " <td>52598</td>\n", + " <td>220.194.79.73</td>\n", + " <td>443</td>\n", + " <td>zhihu</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "<p>10540 rows × 5 columns</p>\n", + "</div>" + ], + "text/plain": [ + " 0 1 2 3 4\n", + "0 192.168.50.150 61251 119.57.174.36 443 douyin\n", + "1 192.168.50.150 61253 119.57.174.36 443 douyin\n", + "2 192.168.50.150 61254 122.14.43.165 443 douyin\n", + "3 192.168.50.150 61258 119.57.174.38 443 douyin\n", + "4 192.168.50.150 61259 119.57.174.38 443 douyin\n", + "... ... ... ... ... ...\n", + "10535 192.168.50.150 52563 182.61.128.198 443 zhihu\n", + "10536 192.168.50.150 52561 182.61.128.198 443 zhihu\n", + "10537 192.168.50.150 52562 182.61.128.198 443 zhihu\n", + "10538 192.168.50.150 52568 121.29.54.234 443 zhihu\n", + "10539 192.168.50.150 52598 220.194.79.73 443 zhihu\n", + "\n", + "[10540 rows x 5 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{('192.168.50.150', 61251, '119.57.174.36', 443): 'douyin',\n", + " ('192.168.50.150', 61253, '119.57.174.36', 443): 'douyin',\n", + " ('192.168.50.150', 61254, '122.14.43.165', 443): 'douyin',\n", + " ('192.168.50.150', 61258, '119.57.174.38', 443): 'douyin',\n", + " ('192.168.50.150', 61259, '119.57.174.38', 443): 'douyin',\n", + " ('192.168.50.150', 61261, '119.57.174.38', 443): 'douyin',\n", + " ('192.168.50.150', 61266, '119.57.174.38', 443): 'douyin',\n", + " ('192.168.50.150', 61267, 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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 +} diff --git a/Experiment/ExpertFeature/tls_cert_length.ipynb b/Experiment/ExpertFeature/tls_cert_length.ipynb new file mode 100644 index 0000000..4abb0e6 --- /dev/null +++ b/Experiment/ExpertFeature/tls_cert_length.ipynb @@ -0,0 +1,542 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "zhihu 3488\n", + "weibo 2705\n", + "douyin 2072\n", + "hupu 1217\n", + "toutiao 1058\n", + "Name: 4, dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "date = '2019-12-20_21'\n", + "root_dir = '/Users/Leo/Documents/github/GradProj/'\n", + "example_label_file = root_dir + '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()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ciper_suits = {\n", + " '1305':0,\n", + " 'C030':1,\n", + "\t'C02C':2,\n", + "\t'C028':3,\n", + "\t'C024':4,\n", + "\t'C014':5,\n", + "\t'C00A':6,\n", + "\t'00A5':7,\n", + "\t'00A3':8,\n", + "\t'00A1':9,\n", + "\t'009F':10,\n", + "\t'006B':11,\n", + "\t'006A':12,\n", + "\t'0069':13,\n", + "\t'0068':14,\n", + "\t'0039':15,\n", + "\t'0038':16,\n", + "\t'0037':17,\n", + "\t'0036':18,\n", + "\t'0088':19,\n", + "\t'0087':20,\n", + "\t'0086':21,\n", + "\t'0085':22,\n", + "\t'C019':23,\n", + "\t'00A7':24,\n", + "\t'006D':25,\n", + "\t'003A':26,\n", + "\t'0089':27,\n", + "\t'C032':28,\n", + "\t'C02E':29,\n", + "\t'C02A':30,\n", + "\t'C026':31,\n", + "\t'C00F':32,\n", + "\t'C005':33,\n", + "\t'009D':34,\n", + "\t'003D':35,\n", + "\t'0035':36,\n", + "\t'0084':37,\n", + "\t'008D':38,\n", + "\t'C02F':39,\n", + "\t'C02B':40,\n", + "\t'C027':41,\n", + "\t'C023':42,\n", + "\t'C013':43,\n", + "\t'C009':44,\n", + "\t'00A4':45,\n", + "\t'00A2':46,\n", + "\t'00A0':47,\n", + "\t'009E':48,\n", + "\t'0067':49,\n", + "\t'0040':50,\n", + "\t'003F':51,\n", + "\t'003E':52,\n", + "\t'0033':53,\n", + "\t'0032':54,\n", + "\t'0031':55,\n", + "\t'0030':56,\n", + "\t'009A':57,\n", + "\t'0099':58,\n", + "\t'0098':59,\n", + "\t'0097':60,\n", + "\t'0045':61,\n", + "\t'0044':62,\n", + "\t'0043':63,\n", + "\t'0042':64,\n", + "\t'C018':65,\n", + "\t'00A6':66,\n", + "\t'006C':67,\n", + "\t'0034':68,\n", + "\t'009B':69,\n", + "\t'0046':70,\n", + "\t'C031':71,\n", + "\t'C02D':72,\n", + "\t'C029':73,\n", + "\t'C025':74,\n", + "\t'C00E':75,\n", + "\t'C004':76,\n", + "\t'009C':77,\n", + "\t'003C':78,\n", + "\t'002F':79,\n", + "\t'0096':80,\n", + "\t'0041':81,\n", + "\t'008C':82,\n", + "\t'C012':83,\n", + "\t'C008':84,\n", + "\t'0016':85,\n", + "\t'0013':86,\n", + "\t'0010':87,\n", + "\t'000D':88,\n", + "\t'C017':89,\n", + "\t'001B':90,\n", + "\t'C00D':91,\n", + "\t'C003':92,\n", + "\t'000A':93,\n", + "\t'0007':94,\n", + "\t'008B':95,\n", + "\t'0021':96,\n", + "\t'001F':97,\n", + "\t'0025':98,\n", + "\t'0023':99,\n", + "\t'C011':100,\n", + "\t'C007':101,\n", + "\t'C016':102,\n", + "\t'0018':103,\n", + "\t'C00C':104,\n", + "\t'C002':105,\n", + "\t'0005':106,\n", + "\t'0004':107,\n", + "\t'008A':108,\n", + "\t'0020':109,\n", + "\t'0024':110,\n", + "\t'C010':111,\n", + "\t'C006':112,\n", + "\t'C015':113,\n", + "\t'C00B':114,\n", + "\t'C001':115,\n", + "\t'003B':116,\n", + "\t'0002':117,\n", + "\t'0001':118,\n", + " '1301':119,\n", + "\t'1302':120,\n", + "\t'1303':121,\n", + "\t'1304':122\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "extensions = { \n", + " 0:0, \n", + " 1:1, \n", + " 2:2, \n", + " 3:3, \n", + " 4:4, \n", + " 5:5, \n", + " 6:6, \n", + " 7:7, \n", + " 8:8, \n", + " 9:9, \n", + " 10:10, \n", + " 11:11, \n", + " 12:12, \n", + " 13:13, \n", + " 14:14, \n", + " 15:15, \n", + " 16:16, \n", + " 17:17, \n", + " 18:18, \n", + " 19:19, \n", + " 20:20, \n", + " 21:21, \n", + " 22:22, \n", + " 23:23, \n", + " 24:24, \n", + " 25:25, \n", + " 26:26, \n", + " 27:27, \n", + " 28:28, \n", + " 29:29, \n", + " 30:30, \n", + " 31:31, \n", + " 35:32, \n", + " 65281:33 \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: 加入cipher suites，extensions特征\n", + "stream_stat_json_file = root_dir + 'DataSet/result/' + date + '/stream_stat.txt'\n", + "stm2cipherDict = dict()\n", + "stm2extenDict = dict()\n", + "with open(stream_stat_json_file) as f:\n", + " lines = f.readlines()\n", + " for line in lines:\n", + " line = json.loads(line)\n", + " flow_key = (line['sip'], line['sport'], line['dip'], line['dport'])\n", + " cipher_suites = line['tls']['cipher_suites']\n", + " extension_list = line['tls']['extensions_list']\n", + " stm2cipherDict[flow_key] = cipher_suites\n", + " stm2extenDict[flow_key] = extension_list" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "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}" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "end\n" + ] + }, + { + "data": { + "text/plain": [ + "'\\nkeys = set(example_label.keys()).difference(set(result_key))\\nexample_keys = example_label_df.iloc[:,0:4].values.copy()\\nfor i,value in enumerate(list(example_keys)):\\n #print(tuple(value))\\n if tuple(value) in keys:\\n print(i)\\n'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example_json_file = root_dir + 'DataSet/result/' + date + '/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", + " ciphers = stm2cipherDict[flow_key]\n", + " extensions_list = stm2extenDict[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", + " \n", + " #tls\n", + " extensions_arr = np.zeros(34, dtype=np.uint8)\n", + " cipher_suits_arr = np.zeros(123, dtype=np.uint8)\n", + " for extension in extensions_list:\n", + " try:\n", + " extensions_arr[extensions[extension]]=1\n", + " except Exception:\n", + " pass\n", + " for cipher in ciphers:\n", + " try:\n", + " cipher = cipher.upper()\n", + " cipher_suits_arr[ciper_suits[cipher]]=1\n", + " except Exception:\n", + " pass\n", + " result += list(cipher_suits_arr)\n", + " result += list(extensions_arr)\n", + " result_data.append(result)\n", + " i += 1\n", + " \n", + "extensions_head = list()\n", + "for i in range(len(extensions)):\n", + " extensions_head.append('extension'+str(i))\n", + "cipher_head = ['cipher'+str(i) for i in range(len(ciper_suits))]\n", + "base_head = ['san_count', 'cert_count', 'cert_length1', 'cert_length2', 'cert_length3','cert_length4']\n", + "header = base_head+cipher_head+extensions_head\n", + "result_df = pd.DataFrame(result_data, columns=header)\n", + "result_df['label'] = np.array(result_label)\n", + "\n", + "print('end')\n", + "'''\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)\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "164\n", + " precision recall f1\n", + "LogisticRegression 0.886296 0.886296 0.886296\n", + "SVM 0.897751 0.897751 0.897751\n", + "GaussianNB 0.694103 0.694103 0.694103\n", + "tree 0.911328 0.911328 0.911328\n", + "RandomForest 0.905388 0.905388 0.905388\n" + ] + }, + { + "data": { + "image/png": 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Udmm8BkRbWPuhqN0yvyCZy/xEktkQxwBzI+KHmRZmVUHS3wA7F+mOZVuXpEkb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\nh7uZWQ453M3Mcuj/Azvm9WexyMnIAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<matplotlib.figure.Figure at 0x1110cfe80>" + ] + }, + "metadata": {}, + "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(len(examples[0]))\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", + " \n", + "scores = [np.mean(precision_score_list), np.mean(recall_score_list), np.mean(f1_score_list)]\n", + "score_df.loc['RandomForest'] = scores\n", + "print(score_df)\n", + "ax = score_df.plot.bar(title='ssl-fingerprint')\n", + "fig = ax.get_figure()\n", + "#fig.savefig('../figure/ssl.svg')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |