596 lines
30 KiB
Plaintext
596 lines
30 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"zhihu 3488\n",
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"weibo 2705\n",
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"douyin 2072\n",
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"hupu 1217\n",
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"toutiao 1058\n",
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"Name: 4, dtype: int64"
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]
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},
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"execution_count": 38,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"date = '2019-12-20_21'\n",
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"root_dir = '/Users/Leo/Documents/github/GradProj/'\n",
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"example_label_file = root_dir + 'DataSet/result/' + date + '/stream_tag.txt'\n",
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"example_label_df = pd.read_table(example_label_file, sep='\\s+', header=None)\n",
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"example_label_df[3] = 443\n",
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"example_label_df[4].value_counts()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import json"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"ciper_suits = {\n",
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" '1305':0,\n",
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" 'C030':1,\n",
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"\t'C02C':2,\n",
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"\t'C028':3,\n",
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"\t'C024':4,\n",
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"\t'C014':5,\n",
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"\t'C00A':6,\n",
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"\t'00A5':7,\n",
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"\t'006A':12,\n",
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"\t'0069':13,\n",
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"\t'0068':14,\n",
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"\t'0039':15,\n",
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"\t'0038':16,\n",
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"\t'0037':17,\n",
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"\t'0036':18,\n",
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"\t'0088':19,\n",
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"\t'0087':20,\n",
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"\t'0086':21,\n",
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"\t'0085':22,\n",
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"\t'C019':23,\n",
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"\t'006D':25,\n",
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"\t'003A':26,\n",
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"\t'0089':27,\n",
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"\t'C032':28,\n",
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"\t'C02E':29,\n",
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"\t'C02A':30,\n",
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"\t'009D':34,\n",
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"\t'003D':35,\n",
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"\t'0035':36,\n",
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"\t'0084':37,\n",
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"\t'008D':38,\n",
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"\t'C02F':39,\n",
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"\t'C02B':40,\n",
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"\t'C027':41,\n",
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"\t'C018':65,\n",
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"\t'006C':67,\n",
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"\t'0034':68,\n",
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"\t'009B':69,\n",
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"\t'0046':70,\n",
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"\t'C031':71,\n",
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"\t'C029':73,\n",
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"\t'C025':74,\n",
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"\t'C00E':75,\n",
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"\t'C004':76,\n",
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"\t'0010':87,\n",
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"\t'000D':88,\n",
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"\t'001B':90,\n",
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"\t'008B':95,\n",
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"\t'0021':96,\n",
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"\t'001F':97,\n",
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"\t'0025':98,\n",
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"\t'0023':99,\n",
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"\t'C011':100,\n",
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"\t'C007':101,\n",
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"\t'C016':102,\n",
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"\t'0018':103,\n",
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"\t'C00C':104,\n",
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"\t'C002':105,\n",
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"\t'0005':106,\n",
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"\t'0004':107,\n",
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"\t'008A':108,\n",
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"\t'0020':109,\n",
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"\t'0024':110,\n",
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"\t'C010':111,\n",
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"\t'C015':113,\n",
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"\t'C00B':114,\n",
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"\t'C001':115,\n",
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"\t'003B':116,\n",
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"\t'0002':117,\n",
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"\t'0001':118,\n",
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" '1301':119,\n",
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"\t'1302':120,\n",
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"\t'1303':121,\n",
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"\t'1304':122\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"extensions = { \n",
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" 0:0, \n",
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" 1:1, \n",
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" 2:2, \n",
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" 3:3, \n",
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" 4:4, \n",
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" 5:5, \n",
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" 6:6, \n",
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" 7:7, \n",
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" 8:8, \n",
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" 9:9, \n",
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" 10:10, \n",
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" 11:11, \n",
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" 12:12, \n",
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" 13:13, \n",
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" 14:14, \n",
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" 15:15, \n",
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" 16:16, \n",
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" 17:17, \n",
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" 18:18, \n",
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" 19:19, \n",
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" 20:20, \n",
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" 21:21, \n",
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" 22:22, \n",
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" 23:23, \n",
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" 24:24, \n",
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" 25:25, \n",
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" 26:26, \n",
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" 27:27, \n",
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" 28:28, \n",
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" 29:29, \n",
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" 30:30, \n",
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" 31:31, \n",
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" 35:32, \n",
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" 65281:33 \n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#TODO: 加入cipher suites，extensions特征\n",
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"stream_stat_json_file = root_dir + 'DataSet/result/' + date + '/stream_stat.txt'\n",
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"stm2cipherDict = dict()\n",
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"stm2extenDict = dict()\n",
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"with open(stream_stat_json_file) as f:\n",
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" lines = f.readlines()\n",
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" for line in lines:\n",
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" line = json.loads(line)\n",
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" flow_key = (line['sip'], line['sport'], line['dip'], line['dport'])\n",
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" cipher_suites = line['tls']['cipher_suites']\n",
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" extension_list = line['tls']['extensions_list']\n",
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" stm2cipherDict[flow_key] = cipher_suites\n",
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" stm2extenDict[flow_key] = extension_list"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"example_label = {tuple(example_label_df.iloc[i,0:4].values):example_label_df.iloc[i,4] for i in example_label_df.index}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"end\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'\\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'"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"example_json_file = root_dir + 'DataSet/result/' + date + '/ssl_stat.txt'\n",
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"example_json_f = open(example_json_file, 'r')\n",
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"#array_shape = (1771,6)\n",
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"result_data = list()\n",
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"result_label = list()\n",
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"result_key = list()\n",
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"i = 0\n",
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"for line in example_json_f.readlines():\n",
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" example_json = ''\n",
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" try:\n",
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" example_json = json.loads(line)\n",
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" except Exception:\n",
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" continue\n",
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" #标签\n",
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" try:\n",
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" flow_key = (example_json['sip'], example_json['sport'], example_json['dip'], example_json['dport'])\n",
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" result_label.append(example_label[flow_key])\n",
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" result_key.append(flow_key)\n",
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" ciphers = stm2cipherDict[flow_key]\n",
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" extensions_list = stm2extenDict[flow_key]\n",
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" except Exception:\n",
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" continue\n",
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" #print(example_json)\n",
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" san_count = 0\n",
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" cert_length = [0,0,0,0]\n",
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" if 'san' in example_json:\n",
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" san_count = len(example_json['san'].split(';'))\n",
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" cert = example_json['Cert']\n",
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" cert_count = cert['cert_count']\n",
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" if cert_count != 0:\n",
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" cert_length = [c['length'] for c in cert['cert_list']]\n",
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" for i in range(4 - len(cert_length)):\n",
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" cert_length.append(0)\n",
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" result = [san_count, cert_count]\n",
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" result += cert_length\n",
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" #print(len(result))\n",
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" \n",
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" #tls\n",
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" extensions_arr = np.zeros(34, dtype=np.uint8)\n",
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" cipher_suits_arr = np.zeros(123, dtype=np.uint8)\n",
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" for extension in extensions_list:\n",
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" try:\n",
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" extensions_arr[extensions[extension]]=1\n",
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" except Exception:\n",
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" pass\n",
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" for cipher in ciphers:\n",
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" try:\n",
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" cipher = cipher.upper()\n",
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" cipher_suits_arr[ciper_suits[cipher]]=1\n",
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" except Exception:\n",
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" pass\n",
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" result += list(cipher_suits_arr)\n",
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" result += list(extensions_arr)\n",
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" result_data.append(result)\n",
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" i += 1\n",
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" \n",
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"extensions_head = list()\n",
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"for i in range(len(extensions)):\n",
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" extensions_head.append('extension'+str(i))\n",
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"cipher_head = ['cipher'+str(i) for i in range(len(ciper_suits))]\n",
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"base_head = ['san_count', 'cert_count', 'cert_length1', 'cert_length2', 'cert_length3','cert_length4']\n",
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"header = base_head+cipher_head+extensions_head\n",
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"result_df = pd.DataFrame(result_data, columns=header)\n",
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"result_df['label'] = np.array(result_label)\n",
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"\n",
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"print('end')\n",
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"'''\n",
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"keys = set(example_label.keys()).difference(set(result_key))\n",
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"example_keys = example_label_df.iloc[:,0:4].values.copy()\n",
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"for i,value in enumerate(list(example_keys)):\n",
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" #print(tuple(value))\n",
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" if tuple(value) in keys:\n",
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" print(i)\n",
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"'''"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 82,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"164\n",
|
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"[2357, 5]\n",
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"0.999400838826\n",
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"0.999400838826\n",
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"0.999400838826\n",
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" precision recall f1\n",
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"LogisticRegression 0.00000 0.00000 0.00000\n",
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"SVM 0.00000 0.00000 0.00000\n",
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"GaussianNB 0.00000 0.00000 0.00000\n",
|
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"tree 0.00000 0.00000 0.00000\n",
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"RandomForest 0.89563 0.89563 0.89563\n"
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]
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},
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{
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"data": {
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"image/png": 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U1A4YA0xp1OYO4JMAkvYg6aZZXMlCzcysfM2Ge0SsA84B7gEWALdExDxJF0oalTa7B1gm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\n4W5mlkMOdzOzHPr/xjLMxOS1hXMAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x10c611ac8>"
|
||
]
|
||
},
|
||
"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
|
||
}
|