{ "cells": [ { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import json\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "date = '2019-12-20_21'\n", "date = 'noProxy/All'\n", "root_dir = '/Users/Leo/Documents/github/GradProj/'\n", "N = 15\n", "Proportion = 0.8" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#stream_tag_path = root_dir + 'DataSet/result/' + date + '/stream_tag.txt'\n", "stream_tag_path = root_dir + 'DataSet/result/' + date + '/stream_tag.txt'\n", "stream_tag_pd = pd.read_table(stream_tag_path, sep='\\s+', header=None)\n", "stream_tag_pd.iloc[:,3]=443\n", "stream_tag_map = {tuple(x[0:4]):x[4] for x in list(stream_tag_pd.values)}\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "34686" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_bursd_l = list()\n", "stream_length_l = list()\n", "stream_type_l = list()\n", "stream_label = list()\n", "#stream_bytes_l = list()\n", "#stream_feature_path = root_dir + 'DataSet/result/' + date + '/stream_stat.txt'\n", "stream_feature_path = root_dir + 'DataSet/result/' + date + '/stream_stat.txt'\n", "stream_feature_f = open(stream_feature_path, 'r')\n", "i = 0\n", "for line in stream_feature_f.readlines():\n", " stream_feature_json = json.loads(line)\n", " try:\n", " key = (stream_feature_json['sip'], stream_feature_json['sport'], stream_feature_json['dip'], stream_feature_json['dport'])\n", " stream_label.append(stream_tag_map[key])\n", " i += 1\n", " except Exception:\n", " continue\n", " packets = stream_feature_json['packets']\n", " tls_message_list = stream_feature_json['tls']['tls_message_list']\n", " stream_length = [tls_message['length'] for tls_message in tls_message_list]\n", " stream_type = [tls_message['type'] for tls_message in tls_message_list]\n", " #stream_length.append((stream_feature_json['sip'], stream_feature_json['sport'], stream_feature_json['dip'], stream_feature_json['dport']))\n", " stream_burst = list()\n", " burst_length = 0\n", "# stream_bytes = list()\n", " old_dir = 0\n", " for j,packet in enumerate(packets):\n", "# stream_bytes.append(packet['bytes'])\n", " if j == 0:\n", " burst_length = packet['bytes']\n", " old_dir = packet['dir']\n", " continue\n", " if old_dir == packet['dir']:\n", " burst_length += packet['bytes']\n", " continue\n", " if old_dir != packet['dir']:\n", " stream_burst.append(burst_length)\n", " old_dir = packet['dir']\n", " burst_length = packet['bytes']\n", "# stream_bytes_l.append(stream_bytes)\n", " stream_burst.append(burst_length)\n", " \n", " #取前10个，20个，30个\n", " if N != -1:\n", " stream_length = stream_length[0:N]\n", " stream_type = stream_type[0:N]\n", " stream_burst = stream_burst[0:N]\n", " \n", " stream_length_l.append(stream_length)\n", " stream_type_l.append(stream_type)\n", " stream_bursd_l.append(stream_burst)\n", "stream_length_array = np.array(stream_length_l)\n", "stream_type_array = np.array(stream_type_l)\n", "stream_burst_array = np.array(stream_bursd_l)\n", "len(stream_length_array)\n", "#stream_bytes_array = np.array(stream_bytes_l)" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[180, 4541, 126, 51, 333, 185265],\n", " [212, 4541, 126, 51, 334, 264020], [181, 4541, 126, 51, 380, 98029],\n", " ...,\n", " [180, 3582, 126, 51, 506, 195, 46, 42, 46, 42, 46, 42, 46, 42, 46],\n", " [517, 133, 941, 444, 1048, 411, 934, 362, 1048, 411, 1093, 343, 1093, 343, 1093],\n", " [517, 3591, 126, 120, 2831, 299, 206, 219, 364, 219, 232, 219, 5577, 261, 4171]], dtype=object)" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_burst_array" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[175, 93, 4091, 333, 4, 70, 1, 40, 1, 40, 328, 5863, 16408, 16408, 16408],\n", " [207, 93, 4091, 333, 4, 70, 1, 40, 1, 40, 329, 2944, 16408, 16408, 16408],\n", " [176, 93, 4091, 333, 4, 70, 1, 40, 1, 40, 375, 2942, 16408, 16408, 16408],\n", " ...,\n", " [175, 89, 3136, 333, 4, 70, 1, 40, 1, 40, 501, 190, 41, 37, 41],\n", " [512, 85, 1, 32, 1, 32, 735, 153, 439, 733, 305, 406, 743, 181, 357],\n", " [512, 98, 3136, 333, 4, 70, 1, 40, 1, 40, 64, 48, 45, 37, 247]], dtype=object)" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_length_array" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 8, 9, 11, 13, 21, 26, 21, 26, 23, 23, 23, 23, 23],\n", " [1, 2, 8, 9, 11, 13, 21, 4, 21, 4, 23, 23, 23, 23, 23],\n", " [1, 2, 8, 9, 11, 13, 21, 26, 21, 26, 23, 23, 23, 23, 23], ...,\n", " [1, 2, 8, 9, 11, 13, 21, 26, 21, 26, 23, 23, 23, 23, 23],\n", " [1, 2, 21, 26, 21, 14, 23, 23, 23, 23, 23, 23, 23, 23, 23],\n", " [1, 2, 8, 9, 11, 13, 21, 0, 21, 26, 23, 23, 23, 23, 23]], dtype=object)" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_type_array" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'alipay': [[180, 4541, 126, 51, 333, 185265],\n", " [212, 4541, 126, 51, 334, 264020],\n", " [181, 4541, 126, 51, 380, 98029],\n", " [212, 4541, 126, 51, 627, 14403],\n", " [212, 4541, 126, 51, 613, 12503],\n", " [172, 3383, 126, 51, 601, 14920],\n", " [212, 4541, 126, 51, 632, 36444],\n", " [212, 4541, 126, 51, 639, 23530],\n", " [212, 4541, 126, 51, 617, 94706],\n", " [212, 4541, 126, 51, 635, 115414],\n", " [212, 4541, 126, 51, 621, 129243],\n", " [212, 4541, 126, 51, 630, 251546],\n", " [212, 4541, 126, 51, 334, 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396, 1041],\n", " [517,\n", " 3666,\n", " 93,\n", " 120,\n", " 1745,\n", " 913,\n", " 729,\n", " 290,\n", " 616,\n", " 252,\n", " 2044,\n", " 20519,\n", " 1465,\n", " 300,\n", " 729],\n", " [517, 2899, 93, 120, 1028, 591, 77],\n", " [307, 3170, 126, 226, 392, 1041],\n", " [517, 5912, 318, 51, 488, 588, 31],\n", " [307, 3170, 126, 226, 409, 1039],\n", " [307, 3170, 126, 226, 395, 1042],\n", " [307, 3170, 126, 226, 417, 1040],\n", " [307, 3170, 126, 226, 414, 1038],\n", " [307, 3170, 126, 226, 418, 1039],\n", " [307, 3170, 126, 226, 417, 1041],\n", " [307, 3170, 126, 226, 418, 1037],\n", " [307, 3170, 126, 226, 418, 1041],\n", " [517, 5823, 609, 19041, 24],\n", " [517, 5823, 606, 19935, 24],\n", " [307, 3170, 126, 226, 418, 1039],\n", " [307, 3170, 126, 226, 381, 1038],\n", " [517, 3666, 93, 120, 4465, 38, 1209, 646, 3969, 344, 3471, 429, 77],\n", " [517,\n", " 2932,\n", " 126,\n", " 120,\n", " 1475,\n", " 204,\n", " 1043,\n", " 195,\n", " 846,\n", " 195,\n", " 783,\n", " 195,\n", " 921,\n", " 195,\n", " 784],\n", " [517,\n", " 2899,\n", " 93,\n", " 120,\n", " 2084,\n", " 31547,\n", " 2067,\n", " 360,\n", " 1075,\n", " 13831,\n", " 709,\n", " 1010,\n", " 1283,\n", " 2040,\n", " 306],\n", " [517,\n", " 2899,\n", " 93,\n", " 120,\n", " 1572,\n", " 909,\n", " 1377,\n", " 13859,\n", " 218,\n", " 2581,\n", " 4085,\n", " 793,\n", " 4435,\n", " 423,\n", " 4021],\n", " ...]}" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stream_type_dict = dict()\n", "stream_length_dict = dict()\n", "stream_burst_dict = dict()\n", "for i in range(len(stream_type_l)):\n", " stream_type = stream_type_l[i]\n", " stream_length = stream_length_l[i]\n", " stream_burst = stream_bursd_l[i]\n", " if stream_label[i] not in stream_type_dict:\n", " stream_type_dict[stream_label[i]] = list()\n", " stream_type_dict[stream_label[i]].append(stream_type)\n", " stream_length_dict[stream_label[i]] = list()\n", " stream_length_dict[stream_label[i]].append(stream_length)\n", " stream_burst_dict[stream_label[i]] = list()\n", " stream_burst_dict[stream_label[i]].append(stream_burst)\n", " else :\n", " stream_type_dict[stream_label[i]].append(stream_type)\n", " stream_length_dict[stream_label[i]].append(stream_length)\n", " stream_burst_dict[stream_label[i]].append(stream_burst)\n", "pd.Series(stream_label).value_counts()\n", "stream_burst_dict" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "def divide_train(stream_dict):\n", " stream_train_dict = dict()\n", " for key, value in stream_dict.items():\n", " random.shuffle(value)\n", " stream_train_dict[key] = value[:int(len(value)*0.75)]\n", " return stream_train_dict\n", "def divide_test(stream_dict):\n", " stream_test_dict = dict()\n", " for key, value in stream_dict.items():\n", " random.shuffle(value)\n", " stream_test_dict[key] = value[int(len(value)*0.75):]\n", " return stream_test_dict\n", "stream_type_train_dict = divide_train(stream_type_dict)\n", "stream_length_train_dict = divide_train(stream_length_dict)\n", "stream_burst_train_dict = divide_train(stream_burst_dict)\n", "stream_type_test_dict = divide_test(stream_type_dict)\n", "stream_length_test_dict = divide_test(stream_length_dict)\n", "stream_burst_test_dict = divide_test(stream_burst_dict)\n", "#len(stream_type_dict)\n", "#stream_burst_train_dict" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#type markov start and end state\n", "type_start_state = dict()\n", "type_end_state = dict()\n", "#transition_matrix = np.zero((27,27))\n", "for key,value in stream_type_train_dict.items():\n", " start_state = pd.Series([line[0] for line in value]).value_counts()\n", " end_state = pd.Series([line[-1] for line in value]).value_counts()\n", " type_start_state[key] = start_state/start_state.sum()\n", " type_end_state[key] = end_state/end_state.sum()\n", "\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#type markov transition_matrix\n", "type_transition_matrix = {key : pd.DataFrame(np.zeros((27,27))) for key in stream_type_train_dict.keys()}\n", "for key,value in stream_type_test_dict.items():\n", " t_value = type_transition_matrix[key]\n", " for line in value:\n", " for i,i_value in enumerate(line):\n", " if i == len(line) - 1:\n", " break\n", " t_value.iloc[i_value,line[i+1]] += 1\n", " type_transition_matrix[key] = t_value.apply(lambda x:x/x.sum(),axis = 'columns').fillna(0)\n", "#type_transition_matrix['weibo']\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#length markov start and end state\n", "length_count = dict()\n", "for key,value in stream_length_train_dict.items():\n", " length_count[key] = dict()\n", " for line in value:\n", " for i in line:\n", " if i not in length_count[key]:\n", " length_count[key][i] = 1\n", " else :\n", " length_count[key][i] += 1\n", " length_count[key] = pd.Series(length_count[key]).sort_values(ascending=False)\n", "#len(length_count['weibo'])\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "length_list = dict()\n", "for key,value in length_count.items():\n", " length_list[key] = dict()\n", " length_sum = 0\n", " pos = 0\n", " for i in range(len(value)):\n", " if length_sum >= value.sum()*Proportion:# or value.iloc[i] <= 1:\n", " break\n", " length_sum += value.iloc[i]\n", " length_list[key][value.index[i]] = pos\n", " pos += 1\n", "#len(length_list['weibo'])\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "stream_length_cdict = dict()\n", "for key,value in stream_length_train_dict.items():\n", " change_list = length_list[key].keys()\n", " for i,line in enumerate(value):\n", " change_line = [min(change_list, key=lambda x:abs(x-i)) for i in line]\n", " value[i] = change_line\n", " stream_length_cdict[key] = value\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "length_start_state = dict()\n", "length_end_state = dict()\n", "for key,value in stream_length_cdict.items():\n", " length_start_state[key] = pd.Series([length_list[key][x[0]] for x in value]).value_counts()\n", " length_end_state[key] = pd.Series([length_list[key][x[-1]] for x in value]).value_counts()\n", " length_start_state[key] = length_start_state[key]/length_start_state[key].sum()\n", " length_end_state[key] = length_end_state[key]/length_end_state[key].sum()\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#length transition matrix\n", "length_transition_matrix = dict()\n", "\n", "for key,value in stream_length_cdict.items():\n", " shape = (len(length_list[key]),len(length_list[key]))\n", " transition = pd.DataFrame(np.zeros(shape))\n", " for line in value:\n", " for i,x in enumerate(line):\n", " if i >= len(line)-1:\n", " break\n", " transition.iloc[length_list[key][x], length_list[key][line[i+1]]] += 1\n", " length_transition_matrix[key] = transition.apply(lambda x:x/x.sum(),axis = 'columns').fillna(0)\n", "#print(length_transition_matrix['chrome'].iloc[5,24])\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "#burst markov start and end state\n", "burst_count = dict()\n", "for key,value in stream_burst_train_dict.items():\n", " burst_count[key] = dict()\n", " for line in value:\n", " for i in line:\n", " if i not in burst_count[key]:\n", " burst_count[key][i] = 1\n", " else :\n", " burst_count[key][i] += 1\n", " burst_count[key] = pd.Series(burst_count[key]).sort_values(ascending=False)\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": true }, "outputs": [], "source": [ "burst_list = dict()\n", "for key,value in burst_count.items():\n", " burst_list[key] = dict()\n", " burst_sum = 0\n", " pos = 0\n", " for i in range(len(value)):\n", " if burst_sum >= value.sum()*Proportion:# or value.iloc[i] <= 1:\n", " break\n", " burst_sum += value.iloc[i]\n", " burst_list[key][value.index[i]] = pos\n", " pos += 1\n", "#len(burst_list['weibo'])" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stream_burst_cdict = dict()\n", "for key,value in stream_burst_train_dict.items():\n", " change_list = burst_list[key].keys()\n", " for i,line in enumerate(value):\n", " change_line = [min(change_list, key=lambda x:abs(x-i)) for i in line]\n", " value[i] = change_line\n", " stream_burst_cdict[key] = value" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": true }, "outputs": [], "source": [ "burst_start_state = dict()\n", "burst_end_state = dict()\n", "#print(stream_burst_cdict['hupu'])\n", "for key,value in stream_burst_cdict.items():\n", " burst_start_state[key] = pd.Series([burst_list[key][x[0]] for x in value]).value_counts()\n", " burst_end_state[key] = pd.Series([burst_list[key][x[-1]] for x in value]).value_counts()\n", " burst_start_state[key] = burst_start_state[key]/burst_start_state[key].sum()\n", " burst_end_state[key] = burst_end_state[key]/burst_end_state[key].sum()\n", "#burst_start_state['weibo']" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#burst transition matrix\n", "burst_transition_matrix = dict()\n", "#print(stream_burst_cdict['hupu'])\n", "#for stream in stream_burst_cdict['hupu']:\n", " # print(len(stream))\n", "for key,value in stream_burst_cdict.items():\n", " shape = (len(burst_list[key]),len(burst_list[key]))\n", " transition = pd.DataFrame(np.zeros(shape))\n", " for line in value:\n", " for i,x in enumerate(line):\n", " if i >= len(line)-1:\n", " break\n", " transition.iloc[burst_list[key][x], burst_list[key][line[i+1]]] += 1\n", " burst_transition_matrix[key] = transition.apply(lambda x:x/x.sum(),axis = 'columns').fillna(0)\n", "#burst_transition_matrix['weibo']" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#type markov train\n", "def train_markov(line, start_state, end_state, transition_matrix):\n", " result = 0\n", " try:\n", " result = start_state[line[0]]\n", " #print('s' + str(result))\n", " except Exception:\n", " pass\n", " for i,x in enumerate(line):\n", " if i == len(line)-1:\n", " try:\n", " result *= end_state[line[-1]]\n", " #print('e' + str(result))\n", " except Exception:\n", " result *=0\n", " return pow(result, 1/len(line))\n", " result *= transition_matrix.iloc[x,line[i+1]]\n", " #print(str(x)+','+str(line[i+1])+':'+str(transition_matrix.iloc[x,line[i+1]]))\n", " #print(result)\n", "type_markov_train = dict()\n", "for key,value in stream_type_train_dict.items():\n", " train_data = list()\n", " for line in value:\n", " data = [train_markov(line, type_start_state[k], type_end_state[k], type_transition_matrix[k]) for k in type_start_state.keys()]\n", " train_data.append(data)\n", " type_markov_train[key] = train_data\n", "#print(type_markov_train['weibo'][0])" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.27335765523346506, 0.0, 0.60976587427481632, 0.0, 0.56999872237874782, 0.28122696451645235, 0.0, 0.10527008591045733, 0.0, 0.0]\n" ] } ], "source": [ "#type markov test\n", "type_markov_test = dict()\n", "for key,value in stream_type_test_dict.items():\n", " test_data = list()\n", " for line in value:\n", " data = [train_markov(line, type_start_state[k], type_end_state[k], type_transition_matrix[k]) for k in type_start_state.keys()]\n", " test_data.append(data)\n", " type_markov_test[key] = test_data\n", "print(type_markov_test['weibo'][0])" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#length markov train\n", "def train_cmarkov(line, start_state, end_state, transition_matrix,key, trans_list):\n", " #print(key)\n", " line = [min(trans_list, key=lambda x:abs(x-i)) for i in line]\n", " #print(line)\n", " result = 0\n", " try:\n", " result = start_state[trans_list[line[0]]]\n", " #print('s' + str(result))\n", " except Exception:\n", " pass\n", " for i,x in enumerate(line):\n", " if i == len(line)-1:\n", " try:\n", " result *= end_state[trans_list[line[-1]]]\n", " #print('e' + ','+str(result))\n", " except Exception:\n", " #print('e')\n", " result *=0\n", " return pow(result, 1/len(line))\n", " result *= transition_matrix.iloc[trans_list[x],trans_list[line[i+1]]]\n", " #print(str(trans_list[x])+','+str(trans_list[line[i+1]])+':'+str(transition_matrix.iloc[trans_list[x],trans_list[line[i+1]]]))\n", " #print(result)\n", "length_markov_train = dict()\n", "for key,value in stream_length_train_dict.items():\n", " train_data = list()\n", " #print(key)\n", " for line in value:\n", " #print(line)\n", " #data_c = train_cmarkov(line, length_start_state[key], length_end_state[key], length_transition_matrix[key], key, length_list[key])\n", " data = [train_cmarkov(line, length_start_state[k], length_end_state[k], length_transition_matrix[k], k, length_list[k]) for k in length_start_state.keys()]\n", " train_data.append(data)\n", " length_markov_train[key] = train_data\n", "#length_markov_train['weibo'][0]" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": true }, "outputs": [], "source": [ "length_markov_test = dict()\n", "for key,value in stream_length_test_dict.items():\n", " test_data = list()\n", " #print(key)\n", " for line in value:\n", " #print(line)\n", " #data_c = test_cmarkov(line, lengthf_start_state[key], length_end_state[key], length_transition_matrix[key], key, length_list[key])\n", " data = [train_cmarkov(line, length_start_state[k], length_end_state[k], length_transition_matrix[k], k, length_list[k]) for k in length_start_state.keys()]\n", " test_data.append(data)\n", " length_markov_test[key] = test_data\n", "#length_markov_test['weibo'][0]" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": true }, "outputs": [], "source": [ "burst_markov_train = dict()\n", "for key,value in stream_burst_train_dict.items():\n", " train_data = list()\n", " #print(key)\n", " for line in value:\n", " #print(line)\n", " #data_c = train_cmarkov(line, burst_start_state[key], burst_end_state[key], burst_transition_matrix[key], key, burst_list[key])\n", " data = [train_cmarkov(line, burst_start_state[k], burst_end_state[k], burst_transition_matrix[k], k, burst_list[k]) for k in burst_start_state.keys()]\n", " train_data.append(data)\n", " burst_markov_train[key] = train_data\n", "#print(burst_markov_train['weibo'][0])" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "burst_markov_test = dict()\n", "for key,value in stream_burst_test_dict.items():\n", " test_data = list()\n", " #print(key)\n", " for line in value:\n", " #print(line)\n", " #data_c = test_cmarkov(line, burst_start_state[key], burst_end_state[key], burst_transition_matrix[key], key, burst_list[key])\n", " data = [train_cmarkov(line, burst_start_state[k], burst_end_state[k], burst_transition_matrix[k], k, burst_list[k]) for k in burst_start_state.keys()]\n", " test_data.append(data)\n", " burst_markov_test[key] = test_data\n", "#print(burst_markov_test['weibo'][0])\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/Leo/Documents/github/GradProj/Experiment/MarkovModel/CsvFile/noProxy/All/train_15_0.8.csv\n", "end2\n" ] } ], "source": [ "if N != -1:\n", " train_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/train_' + str(N) + \"_\" + str(Proportion) + '.csv'\n", "else:\n", " train_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/train_all_' + str(Proportion) + '.csv'\n", "print(train_path)\n", "train_data_dict = dict()\n", "for key,value in type_markov_train.items():\n", " data = pd.concat([pd.DataFrame(value),pd.DataFrame(length_markov_train[key]), pd.DataFrame(burst_markov_train[key])], axis=1)\n", " data['label'] = key\n", " train_data_dict[key] = data\n", "train_data_df = pd.concat([value for value in train_data_dict.values()],axis=0)\n", "train_data_df.to_csv(train_path)\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "end2\n" ] } ], "source": [ "if N != -1:\n", " test_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/test_' + str(N) + \"_\" + str(Proportion) + '.csv'\n", "else:\n", " test_path = root_dir + 'Experiment/MarkovModel/CsvFile/' + date + '/test_all_' + str(Proportion) + '.csv'\n", " \n", "test_data_dict = dict()\n", "for key,value in type_markov_test.items():\n", " data = pd.concat([pd.DataFrame(value),pd.DataFrame(length_markov_test[key]), pd.DataFrame(burst_markov_test[key])], axis=1)\n", " data['label'] = key\n", " test_data_dict[key] = data\n", "test_data_df = pd.concat([value for value in test_data_dict.values()],axis=0)\n", "test_data_df.to_csv(test_path)\n", "print('end2')" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.62448132780082988" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#randomforest\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.svm import SVC\n", "x_train = train_data_df.iloc[:,0:5].values.copy()\n", "y_train = train_data_df.iloc[:,-1].values.copy()\n", "classifer = RandomForestClassifier()\n", "classifer.fit(x_train,y_train)\n", "x_test = test_data_df.iloc[:,0:5].values.copy()\n", "y_test = test_data_df.iloc[:,-1].values.copy()\n", "y_pred = classifer.predict(x_test)\n", "accuracy_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "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 }