{ "cells": [ { "cell_type": "code", "execution_count": 38, "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": 38, "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": 39, "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": 40, "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": 41, "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": 42, "metadata": { "collapsed": true }, "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": 43, "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": 44, "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": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "164\n", "[2357, 5]\n", "0.999400838826\n", "0.999400838826\n", "0.999400838826\n", " precision recall f1\n", "LogisticRegression 0.00000 0.00000 0.00000\n", "SVM 0.00000 0.00000 0.00000\n", "GaussianNB 0.00000 0.00000 0.00000\n", "tree 0.00000 0.00000 0.00000\n", "RandomForest 0.89563 0.89563 0.89563\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "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", "\n", "\n", "def my_pred(y_pred, y_test, proba):\n", " y_pred1 = list()\n", " y_test1 = list()\n", " [rows, clos] = proba.shape\n", " print([rows, clos])\n", " right_count = 0\n", " wrong_count = 0\n", " for i in range(rows):\n", " temp = max(proba[i])\n", " if temp < 0.95:\n", " continue\n", " y_pred1.append(y_pred[i])\n", " y_test1.append(y_test[i])\n", " f1 = f1_score(y_test1, y_pred1, average='micro')\n", " recall = recall_score(y_test1, y_pred1, average='micro')\n", " precision = precision_score(y_test1, y_pred1, average='micro')\n", " print(precision)\n", " print(recall)\n", " print(f1)\n", " \n", "\n", "'''\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", "\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", " proba = classifer.predict_proba(x_test)\n", " my_pred(y_pred, y_test, proba)\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')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "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 }