├── .gitattributes ├── pics ├── corr_features.png ├── roc_curve_sflow.png ├── acc_f1_auc_lgb_xgb.png ├── feature_importance.png ├── protocol_value_counts.png ├── noserial_Protocol_count_label.png └── noserial_PSH Flag Cnt_count_label.png ├── models └── lgb_trained_model.pkl ├── sflow_traffic ├── define_flow.py └── get_data_from_sflow.py ├── README.md ├── .gitignore ├── datasets ├── collected_benign_traffic.csv └── collected_ddos_traffic.csv ├── dataset_understanding.ipynb ├── LICENSE ├── feature_engineering.ipynb └── draw_pics.ipynb /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /pics/corr_features.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bil369/IDS2018-DDoS-Traffic-Classify/HEAD/pics/corr_features.png -------------------------------------------------------------------------------- /pics/roc_curve_sflow.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bil369/IDS2018-DDoS-Traffic-Classify/HEAD/pics/roc_curve_sflow.png -------------------------------------------------------------------------------- /models/lgb_trained_model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bil369/IDS2018-DDoS-Traffic-Classify/HEAD/models/lgb_trained_model.pkl -------------------------------------------------------------------------------- /pics/acc_f1_auc_lgb_xgb.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bil369/IDS2018-DDoS-Traffic-Classify/HEAD/pics/acc_f1_auc_lgb_xgb.png -------------------------------------------------------------------------------- /pics/feature_importance.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bil369/IDS2018-DDoS-Traffic-Classify/HEAD/pics/feature_importance.png -------------------------------------------------------------------------------- /pics/protocol_value_counts.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bil369/IDS2018-DDoS-Traffic-Classify/HEAD/pics/protocol_value_counts.png -------------------------------------------------------------------------------- /pics/noserial_Protocol_count_label.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bil369/IDS2018-DDoS-Traffic-Classify/HEAD/pics/noserial_Protocol_count_label.png -------------------------------------------------------------------------------- /pics/noserial_PSH Flag Cnt_count_label.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Bil369/IDS2018-DDoS-Traffic-Classify/HEAD/pics/noserial_PSH Flag Cnt_count_label.png -------------------------------------------------------------------------------- /sflow_traffic/define_flow.py: -------------------------------------------------------------------------------- 1 | # define flow called 'test' to get the flow information. 2 | import json 3 | import requests 4 | 5 | flow_defination = {'keys': 'ipsource,ipdestination,tcpsourceport,tcpdestinationport,ip_offset,tcp_offset,tcpwindow,tcppayloadbytes', 6 | 'value': 'bytes', 'log': True} 7 | response = requests.put('http://localhost:9999/flow/test/json', data=json.dumps(flow_defination)) 8 | print(response.status_code) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # IDS2018-DDoS-Traffic-Classify 2 | 3 | 杭电综合项目实践源代码，主要包括： 4 | - **模型训练**：基于公开的入侵检测数据集IDS 2018中Thursday-15-02这一天的数据，使用LightGBM和XGBoost算法训练DDoS流量分类模型，实现对正常流量和DDoS流量的分类，模型五折交叉验证AUC达到0.99 5 | - **流量采集与分类**：基于软件定义网络虚拟环境Mininet搭建了一个简单的网络拓扑，通过使用sFlow RT采集网络流量，并利用DDoS流量分类模型进行检测，模型AUC为0.81 6 | 7 | ## 目录 8 | - [依赖](#依赖) 9 | - [使用](#使用) 10 | - [数据集下载](#数据集下载) 11 | - [模型训练](#模型训练) 12 | - [流量采集与分类](#流量采集与分类) 13 | - [License](#License) 14 | 15 | ## 依赖 16 | 17 | - Anaconda 3环境 18 | - pip install lightgbm 19 | - pip install xgboost 20 | 21 | ## 使用 22 | 23 | ### 数据集下载 24 | 25 | 数据集存储在AWS上，因此首先需要安装[AWS CLI](https://aws.amazon.com/cn/cli/)。 26 | 27 | 安装完成后，在命令行中输入以下命令查看目录下的所有文件： 28 | ```shell 29 | aws s3 ls --no-sign-request "s3://cse-cic-ids2018" --recursive --human-readable --summarize 30 | ``` 31 | 32 | 下载数据集： 33 | ```shell 34 | aws s3 cp --no-sign-request "s3://cse-cic-ids2018/Processed Traffic Data for ML Algorithms/Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv" G:\ 35 | ``` 36 | 37 | AWS CLI下载并不稳定，可以直接访问http://cse-cic-ids2018.s3.amazonaws.com/Processed%20Traffic%20Data%20for%20ML%20Algorithms/Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv 下载。 38 | 39 | ### 模型训练 40 | 41 | | 文件 | 内容 | 42 | | - | - | 43 | | dataset_understanding.ipynb | 数据理解 | 44 | | EDA.ipynb | 数据探索性分析 | 45 | | feature_engineering.ipynb | 特征工程 | 46 | | baseline.ipynb | initial baseline | 47 | | baseline2.ipynb | final baseline | 48 | 49 | ### 流量采集 50 | 51 | 两台虚拟机，安装Ubuntu，一台作为sFlow Collector，另一台作为sFlow Agent。 52 | 53 | #### sFlow Collector 54 | 55 | 配置JDK环境，下载sFlow RT 3.0，安装sFlow RT流量分析APP flow-trend和browse-metrics： 56 | ```shell 57 | ./sflow-rt/get-app.sh sflow-rt flow-trend 58 | ./sflow-rt/get-app.sh sflow-rt browse-metrics 59 | ``` 60 | 启动 61 | ```shell 62 | ./sflow-rt/start.sh 63 | ``` 64 | 访问localhost:8008打开Web界面，sFlow Collector接收sFlow Agent数据在6343端口。 65 | 66 | #### sFlow Agent 67 | 68 | 配置Mininet环境，mn创建拓扑。 69 | 70 | 设置交换机开启sFlow功能： 71 | ```shell 72 | ovs-vsctl -- --id=@sflow create sflow agent=eth0 target=\"192.168.222.129:6343\" sampling=10 polling=1 -- set bridge s1 sflow=@sflow 73 | ``` 74 | 查看已配置的sFlow Agent： 75 | ```shell 76 | ovs-vsctl list sflow 77 | ``` 78 | 79 | 查看网络链路情况： 80 | ```shell 81 | ip link 82 | ``` 83 | 84 | #### 定义流 85 | 86 | ./sflow_traffic/define_flow.py 87 | 88 | #### 采集流量 89 | 90 | ./sflow_traffic/get_data_from_sflow.py 91 | 92 | ### 流量分类 93 | 94 | ./sflow_traffic/sflow_traffic_classift.ipynb 95 | 96 | ## License 97 | 98 | [GPL](https://github.com/Bil369/IDS2018-DDoS-Traffic-Classify/blob/main/LICENSE) © [Bil369](https://github.com/Bil369) -------------------------------------------------------------------------------- /sflow_traffic/get_data_from_sflow.py: -------------------------------------------------------------------------------- 1 | # get data through sFlow RT 2 | import json 3 | import pandas as pd 4 | import requests 5 | 6 | IS_BENIGN = True 7 | 8 | # Fwd_Seg_Size_Min 9 | fwd_seg_size_min = [] 10 | # Dst_Port 11 | dst_port = [] 12 | # Fwd_Header_Len 13 | fwd_header_len = [] 14 | # Init_Fwd_Win_Byts 15 | init_fwd_win_byts = [] 16 | while len(fwd_seg_size_min) < 100: 17 | print('Data get: [{0}]/[100]'.format(len(fwd_seg_size_min))) 18 | response = requests.get('http://localhost:9999/activeflows/192.168.222.128/test/json') 19 | print('Response status code:', response.status_code) 20 | response_json = response.json() 21 | for i in range(len(response_json)): 22 | if len(fwd_seg_size_min) >= 100: 23 | break 24 | ipsource, ipdestination, tcpsourceport, tcpdestinationport, ip_offset, tcp_offset, tcpwindow, tcppayloadbytes = response_json[i]['key'].split(',') 25 | if IS_BENIGN: 26 | fwd_seg_size_min.append(int(tcp_offset) + int(tcppayloadbytes)) 27 | dst_port.append(int(tcpdestinationport)) 28 | fwd_header_len.append(int(ip_offset) + int(tcp_offset)) 29 | init_fwd_win_byts.append(int(tcpwindow)) 30 | else: 31 | # only the forward traffic is ddos traffic when attack the server(10.0.0.1) 32 | if ipdestination == '10.0.0.1': 33 | fwd_seg_size_min.append(int(tcp_offset) + int(tcppayloadbytes)) 34 | dst_port.append(int(tcpdestinationport)) 35 | fwd_header_len.append(int(ip_offset) + int(tcp_offset)) 36 | init_fwd_win_byts.append(int(tcpwindow)) 37 | 38 | # Flow_Byts/s 39 | flow_byts = [] 40 | while len(flow_byts) < 100: 41 | print('Data get: [{0}]/[100]'.format(len(flow_byts))) 42 | response = requests.get('http://localhost:9999/metric/192.168.222.128/avg:ifoutoctets/json') 43 | print('Response status code:', response.status_code) 44 | response_json = response.json() 45 | flow_byts.append(float(response_json[0]['metricValue'])) 46 | 47 | collected_data = pd.DataFrame({'Fwd_Seg_Size_Min': fwd_seg_size_min, 48 | 'Dst_Port': dst_port, 49 | 'Fwd_Header_Len': fwd_header_len, 50 | 'Init_Fwd_Win_Byts': init_fwd_win_byts, 51 | 'Flow_Byts/s': flow_byts}) 52 | if IS_BENIGN: 53 | collected_data.to_csv('./datasets/collected_benign_traffic.csv', index=False) 54 | else: 55 | collected_data.to_csv('./datasets/collected_ddos_traffic.csv', index=False) 56 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # celery beat schedule file 95 | celerybeat-schedule 96 | 97 | # SageMath parsed files 98 | *.sage.py 99 | 100 | # Environments 101 | .env 102 | .venv 103 | env/ 104 | venv/ 105 | ENV/ 106 | env.bak/ 107 | venv.bak/ 108 | 109 | # Spyder project settings 110 | .spyderproject 111 | .spyproject 112 | 113 | # Rope project settings 114 | .ropeproject 115 | 116 | # mkdocs documentation 117 | /site 118 | 119 | # mypy 120 | .mypy_cache/ 121 | .dmypy.json 122 | dmypy.json 123 | 124 | # Pyre type checker 125 | .pyre/ 126 | datasets/Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv 127 | learning/baseline_template.md 128 | learning/draft.ipynb 129 | learning/EDA_template.md 130 | example.html 131 | -------------------------------------------------------------------------------- /datasets/collected_benign_traffic.csv: -------------------------------------------------------------------------------- 1 | Fwd_Seg_Size_Min,Dst_Port,Fwd_Header_Len,Init_Fwd_Win_Byts,Flow_Byts/s,Label 2 | 34,42840,48,85,537.1295371,0 3 | 34,80,48,42340,537.6666667,0 4 | 34,42830,48,85,494.6559786,0 5 | 173,80,48,83,493.1734932,0 6 | 34,42824,48,43440,538.2048715,0 7 | 34,80,48,42340,495.152123,0 8 | 34,42822,48,85,493.1734932,0 9 | 173,80,48,83,495.152123,0 10 | 188,42816,48,85,0,0 11 | 34,42818,48,43440,0,0 12 | 34,80,48,83,0,0 13 | 188,42812,48,85,0,0 14 | 188,42810,48,85,0,0 15 | 34,80,48,83,0,0 16 | 34,42812,48,85,0,0 17 | 173,80,48,83,0,0 18 | 34,80,48,42340,0,0 19 | 34,80,48,83,0,0 20 | 34,42808,48,85,0,0 21 | 34,80,48,83,0,0 22 | 34,42802,48,85,0,0 23 | 173,80,48,83,0,0 24 | 34,80,48,42340,0,0 25 | 34,80,48,42340,537.6666667,0 26 | 34,80,48,83,537.6666667,0 27 | 34,42792,48,85,494.1608275,0 28 | 188,42788,48,85,494.1608275,0 29 | 34,80,48,42340,537.6666667,0 30 | 34,42786,48,43440,494.1608275,0 31 | 34,80,48,83,24.6174318,0 32 | 34,42786,48,85,0,0 33 | 188,42782,48,85,0,0 34 | 34,42778,48,85,0,0 35 | 173,80,48,83,0,0 36 | 34,42776,48,85,0,0 37 | 173,80,48,83,0,0 38 | 34,42772,48,85,0,0 39 | 188,42768,48,85,0,0 40 | 34,42766,48,43440,0,0 41 | 34,80,48,83,0,0 42 | 34,80,48,83,0,0 43 | 34,42762,48,85,0,0 44 | 34,80,48,42340,0,0 45 | 34,80,48,83,0,0 46 | 34,42756,48,43440,0,0 47 | 34,80,48,42340,0,0 48 | 34,80,48,42340,0,0 49 | 34,80,48,83,0,0 50 | 188,42746,48,85,0,0 51 | 34,42746,48,85,0,0 52 | 34,80,48,83,0,0 53 | 34,42744,48,85,0,0 54 | 34,42742,48,43440,469,0 55 | 188,42734,48,85,493.6666667,0 56 | 34,80,48,42340,540.3685092,0 57 | 34,80,48,83,492.6813041,0 58 | 34,42734,48,85,536.5934797,0 59 | 34,80,48,83,536.5934797,0 60 | 34,80,48,42340,0,0 61 | 34,42730,48,85,0,0 62 | 34,80,48,42340,0,0 63 | 188,42724,48,85,0,0 64 | 34,80,48,42340,0,0 65 | 34,42726,48,85,0,0 66 | 34,42724,48,85,0,0 67 | 34,42724,48,43440,0,0 68 | 34,80,48,83,0,0 69 | 34,80,48,42340,0,0 70 | 34,80,48,42340,0,0 71 | 34,80,48,42340,0,0 72 | 34,80,48,83,0,0 73 | 34,80,48,42340,0,0 74 | 34,42712,48,43440,493.1734932,0 75 | 34,80,48,42340,537.1295371,0 76 | 34,80,48,83,493.6666667,0 77 | 34,80,48,42340,493.6666667,0 78 | 34,80,48,83,537.6666667,0 79 | 34,80,48,83,538.744155,0 80 | 34,80,48,42340,494.1608275,0 81 | 34,80,48,83,494.1608275,0 82 | 34,80,48,83,537.1295371,0 83 | 173,80,48,83,537.6666667,0 84 | 34,80,48,83,493.6666667,0 85 | 34,80,48,83,493.6666667,0 86 | 34,42840,48,85,494.1608275,0 87 | 34,80,48,42340,494.1608275,0 88 | 34,42830,48,85,538.2048715,0 89 | 173,80,48,83,537.6666667,0 90 | 34,42824,48,43440,493.1734932,0 91 | 34,80,48,42340,493.6666667,0 92 | 34,42822,48,85,494.1608275,0 93 | 173,80,48,83,0,0 94 | 188,42816,48,85,493.6666667,0 95 | 34,42818,48,43440,493.6666667,0 96 | 34,80,48,83,537.6666667,0 97 | 188,42812,48,85,537.6666667,0 98 | 188,42810,48,85,537.1295371,0 99 | 34,80,48,83,494.1608275,0 100 | 34,42812,48,85,493.1734932,0 101 | 173,80,48,83,537.6666667,0 102 | -------------------------------------------------------------------------------- /datasets/collected_ddos_traffic.csv: -------------------------------------------------------------------------------- 1 | Fwd_Seg_Size_Min,Dst_Port,Fwd_Header_Len,Init_Fwd_Win_Byts,Flow_Byts/s,Label 2 | 714,80,48,83,35124.12412,1 3 | 580,80,48,83,34753.08642,1 4 | 681,80,48,83,33347.65235,1 5 | 614,80,48,83,31288.66233,1 6 | 354,80,48,83,33426.51971,1 7 | 465,80,48,83,24539.17663,1 8 | 537,80,48,83,34015.34868,1 9 | 539,80,48,83,38189.66667,1 10 | 513,80,48,83,37868.13187,1 11 | 349,80,48,83,33402.12695,1 12 | 376,80,48,83,30050.71738,1 13 | 399,80,48,83,39195.39078,1 14 | 327,80,48,83,32800.5328,1 15 | 424,80,48,83,39500.83417,1 16 | 481,80,48,83,38520.81252,1 17 | 460,80,48,83,34872.84197,1 18 | 34,80,48,42340,37321.30929,1 19 | 34,80,48,83,36791.16866,1 20 | 34,80,48,42340,35806.1031,1 21 | 34,80,48,42340,32112.11211,1 22 | 34,80,48,42340,38703.59281,1 23 | 34,80,48,42340,38260.10018,1 24 | 34,80,48,83,40770.10344,1 25 | 34,80,48,82,38898.4349,1 26 | 34,80,48,83,36964.03596,1 27 | 34,80,48,42340,31447.22779,1 28 | 34,80,48,82,35748.74875,1 29 | 34,80,48,82,30212.09085,1 30 | 34,80,48,82,28326.35427,1 31 | 34,80,48,83,30499.66667,1 32 | 34,80,48,42340,41850.7014,1 33 | 34,80,48,42340,34367.50419,1 34 | 34,80,48,83,36548.7642,1 35 | 34,80,48,83,34483.33333,1 36 | 34,80,48,82,35736.19428,1 37 | 34,80,48,42340,31201.73681,1 38 | 34,80,48,42340,38843.84093,1 39 | 34,80,48,83,8658.674659,1 40 | 34,80,48,42340,5456.122789,1 41 | 34,80,48,42340,6304.477612,1 42 | 34,80,48,83,5098.901099,1 43 | 34,80,48,83,4236.430236,1 44 | 34,80,48,83,5394,1 45 | 34,80,48,82,4547.808765,1 46 | 491,80,48,83,3932.067932,1 47 | 442,80,48,83,4059.94006,1 48 | 404,80,48,83,3679.65368,1 49 | 378,80,48,83,11404,1 50 | 470,80,48,83,1959.373959,1 51 | 537,80,48,83,1911.551155,1 52 | 615,80,48,83,2376.408217,1 53 | 540,80,48,83,2852,1 54 | 434,80,48,83,2584.082584,1 55 | 505,80,48,83,2687.312687,1 56 | 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34,80,48,42340,2202.666667,1 88 | 34,80,48,83,2452.214452,1 89 | 404,80,48,83,2140.021978,1 90 | 378,80,48,83,1529.475475,1 91 | 470,80,48,83,2038.036036,1 92 | 537,80,48,83,2017.964072,1 93 | 615,80,48,83,1992.046154,1 94 | 540,80,48,83,1540,1 95 | 34,80,48,83,264,1 96 | 34,80,48,83,22.02202202,1 97 | 434,80,48,83,0,1 98 | 505,80,48,83,0,1 99 | 416,80,48,83,21.89054726,1 100 | 34,80,48,42340,44.04404404,1 101 | 525,80,48,83,22.02202202,1 102 | -------------------------------------------------------------------------------- /dataset_understanding.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 5, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "pd.set_option('display.max_columns', None)" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 2, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "data = pd.read_csv('datasets/Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv')" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 3, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "data": { 37 | "text/plain": [ 38 | "(1048575, 80)" 39 | ] 40 | }, 41 | "execution_count": 3, 42 | "metadata": {}, 43 | "output_type": "execute_result" 44 | } 45 | ], 46 | "source": [ 47 | "data.shape" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 6, 53 | "metadata": {}, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "text/html": [ 58 | "
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Dst PortProtocolTimestampFlow DurationTot Fwd PktsTot Bwd PktsTotLen Fwd PktsTotLen Bwd PktsFwd Pkt Len MaxFwd Pkt Len MinFwd Pkt Len MeanFwd Pkt Len StdBwd Pkt Len MaxBwd Pkt Len MinBwd Pkt Len MeanBwd Pkt Len StdFlow Byts/sFlow Pkts/sFlow IAT MeanFlow IAT StdFlow IAT MaxFlow IAT MinFwd IAT TotFwd IAT MeanFwd IAT StdFwd IAT MaxFwd IAT MinBwd IAT TotBwd IAT MeanBwd IAT StdBwd IAT MaxBwd IAT MinFwd PSH FlagsBwd PSH FlagsFwd URG FlagsBwd URG FlagsFwd Header LenBwd Header LenFwd Pkts/sBwd Pkts/sPkt Len MinPkt Len MaxPkt Len MeanPkt Len StdPkt Len VarFIN Flag CntSYN Flag CntRST Flag CntPSH Flag CntACK Flag CntURG Flag CntCWE Flag CountECE Flag CntDown/Up RatioPkt Size AvgFwd Seg Size AvgBwd Seg Size AvgFwd Byts/b AvgFwd Pkts/b AvgFwd Blk Rate AvgBwd Byts/b AvgBwd Pkts/b AvgBwd Blk Rate AvgSubflow Fwd PktsSubflow Fwd BytsSubflow Bwd PktsSubflow Bwd BytsInit Fwd Win BytsInit Bwd Win BytsFwd Act Data PktsFwd Seg Size MinActive MeanActive StdActive MaxActive MinIdle MeanIdle StdIdle MaxIdle MinLabel
00015/02/2018 08:25:181126411583000000.0000000.000000000.0000000.0000000.0000000.02663356320579.007.042784e+0256321077563200811126411585.632058e+077.042784e+02563210775632008100.000000e+000.000000e+00000000000.0266330.000000000.0000000.0000000.0000000000000000.00.0000000.0000000000003000-1-1000.00.0000000056320579.07.042784e+025632107756320081Benign
122615/02/2018 08:29:05373667621412216829937120154.857143254.8552709760249.416667395.929392138.1174000.6958061494670.483.894924e+06156174157373667622.874366e+065.104444e+061561741540373667303.396975e+065.564224e+061593676289300004564160.3746650.3211410976191.148148320.122898102478.669516000100000198.5154.857143249.416667000000142168122993292002328321024353.0649038.754495160118332156911431221.03.644991e+06156174158960247Benign
247514615/02/2018 08:29:425432064064032.00000045.254834000.0000000.000000117863.7200743683.241252543.000.000000e+005435435435.430000e+020.000000e+0054354300.000000e+000.000000e+000010006403683.2412520.00000006442.66666736.9504171365.33333301001000064.032.0000000.00000000000026400244-10320.00.000000000.00.000000e+0000Benign
30015/02/2018 08:28:071126407033000000.0000000.000000000.0000000.0000000.0000000.02663356320351.503.669884e+0256320611563200921126407035.632035e+073.669884e+02563206115632009200.000000e+000.000000e+00000000000.0266330.000000000.0000000.0000000.0000000000000000.00.0000000.0000000000003000-1-1000.00.0000000056320351.53.669884e+025632061156320092Benign
40015/02/2018 08:30:561126408743000000.0000000.000000000.0000000.0000000.0000000.02663356320437.007.198347e+0256320946563199281126408745.632044e+077.198347e+02563209465631992800.000000e+000.000000e+00000000000.0266330.000000000.0000000.0000000.0000000000000000.00.0000000.0000000000003000-1-1000.00.0000000056320437.07.198347e+025632094656319928Benign
\n", 576 | "
" 577 | ], 578 | "text/plain": [ 579 | " Dst Port Protocol Timestamp Flow Duration Tot Fwd Pkts \\\n", 580 | "0 0 0 15/02/2018 08:25:18 112641158 3 \n", 581 | "1 22 6 15/02/2018 08:29:05 37366762 14 \n", 582 | "2 47514 6 15/02/2018 08:29:42 543 2 \n", 583 | "3 0 0 15/02/2018 08:28:07 112640703 3 \n", 584 | "4 0 0 15/02/2018 08:30:56 112640874 3 \n", 585 | "\n", 586 | " Tot Bwd Pkts TotLen Fwd Pkts TotLen Bwd Pkts Fwd Pkt Len Max \\\n", 587 | "0 0 0 0 0 \n", 588 | "1 12 2168 2993 712 \n", 589 | "2 0 64 0 64 \n", 590 | "3 0 0 0 0 \n", 591 | "4 0 0 0 0 \n", 592 | "\n", 593 | " Fwd Pkt Len Min Fwd Pkt Len Mean Fwd Pkt Len Std Bwd Pkt Len Max \\\n", 594 | "0 0 0.000000 0.000000 0 \n", 595 | "1 0 154.857143 254.855270 976 \n", 596 | "2 0 32.000000 45.254834 0 \n", 597 | "3 0 0.000000 0.000000 0 \n", 598 | "4 0 0.000000 0.000000 0 \n", 599 | "\n", 600 | " Bwd Pkt Len Min Bwd Pkt Len Mean Bwd Pkt Len Std Flow Byts/s \\\n", 601 | "0 0 0.000000 0.000000 0.000000 \n", 602 | "1 0 249.416667 395.929392 138.117400 \n", 603 | "2 0 0.000000 0.000000 117863.720074 \n", 604 | "3 0 0.000000 0.000000 0.000000 \n", 605 | "4 0 0.000000 0.000000 0.000000 \n", 606 | "\n", 607 | " Flow Pkts/s Flow IAT Mean Flow IAT Std Flow IAT Max Flow IAT Min \\\n", 608 | "0 0.026633 56320579.00 7.042784e+02 56321077 56320081 \n", 609 | "1 0.695806 1494670.48 3.894924e+06 15617415 7 \n", 610 | "2 3683.241252 543.00 0.000000e+00 543 543 \n", 611 | "3 0.026633 56320351.50 3.669884e+02 56320611 56320092 \n", 612 | "4 0.026633 56320437.00 7.198347e+02 56320946 56319928 \n", 613 | "\n", 614 | " Fwd IAT Tot Fwd IAT Mean Fwd IAT Std Fwd IAT Max Fwd IAT Min \\\n", 615 | "0 112641158 5.632058e+07 7.042784e+02 56321077 56320081 \n", 616 | "1 37366762 2.874366e+06 5.104444e+06 15617415 40 \n", 617 | "2 543 5.430000e+02 0.000000e+00 543 543 \n", 618 | "3 112640703 5.632035e+07 3.669884e+02 56320611 56320092 \n", 619 | "4 112640874 5.632044e+07 7.198347e+02 56320946 56319928 \n", 620 | "\n", 621 | " Bwd IAT Tot Bwd IAT Mean Bwd IAT Std Bwd IAT Max Bwd IAT Min \\\n", 622 | "0 0 0.000000e+00 0.000000e+00 0 0 \n", 623 | "1 37366730 3.396975e+06 5.564224e+06 15936762 893 \n", 624 | "2 0 0.000000e+00 0.000000e+00 0 0 \n", 625 | "3 0 0.000000e+00 0.000000e+00 0 0 \n", 626 | "4 0 0.000000e+00 0.000000e+00 0 0 \n", 627 | "\n", 628 | " Fwd PSH Flags Bwd PSH Flags Fwd URG Flags Bwd URG Flags Fwd Header Len \\\n", 629 | "0 0 0 0 0 0 \n", 630 | "1 0 0 0 0 456 \n", 631 | "2 1 0 0 0 64 \n", 632 | "3 0 0 0 0 0 \n", 633 | "4 0 0 0 0 0 \n", 634 | "\n", 635 | " Bwd Header Len Fwd Pkts/s Bwd Pkts/s Pkt Len Min Pkt Len Max \\\n", 636 | "0 0 0.026633 0.000000 0 0 \n", 637 | "1 416 0.374665 0.321141 0 976 \n", 638 | "2 0 3683.241252 0.000000 0 64 \n", 639 | "3 0 0.026633 0.000000 0 0 \n", 640 | "4 0 0.026633 0.000000 0 0 \n", 641 | "\n", 642 | " Pkt Len Mean Pkt Len Std Pkt Len Var FIN Flag Cnt SYN Flag Cnt \\\n", 643 | "0 0.000000 0.000000 0.000000 0 0 \n", 644 | "1 191.148148 320.122898 102478.669516 0 0 \n", 645 | "2 42.666667 36.950417 1365.333333 0 1 \n", 646 | "3 0.000000 0.000000 0.000000 0 0 \n", 647 | "4 0.000000 0.000000 0.000000 0 0 \n", 648 | "\n", 649 | " RST Flag Cnt PSH Flag Cnt ACK Flag Cnt URG Flag Cnt CWE Flag Count \\\n", 650 | "0 0 0 0 0 0 \n", 651 | "1 0 1 0 0 0 \n", 652 | "2 0 0 1 0 0 \n", 653 | "3 0 0 0 0 0 \n", 654 | "4 0 0 0 0 0 \n", 655 | "\n", 656 | " ECE Flag Cnt Down/Up Ratio Pkt Size Avg Fwd Seg Size Avg \\\n", 657 | "0 0 0 0.0 0.000000 \n", 658 | "1 0 0 198.5 154.857143 \n", 659 | "2 0 0 64.0 32.000000 \n", 660 | "3 0 0 0.0 0.000000 \n", 661 | "4 0 0 0.0 0.000000 \n", 662 | "\n", 663 | " Bwd Seg Size Avg Fwd Byts/b Avg Fwd Pkts/b Avg Fwd Blk Rate Avg \\\n", 664 | "0 0.000000 0 0 0 \n", 665 | "1 249.416667 0 0 0 \n", 666 | "2 0.000000 0 0 0 \n", 667 | "3 0.000000 0 0 0 \n", 668 | "4 0.000000 0 0 0 \n", 669 | "\n", 670 | " Bwd Byts/b Avg Bwd Pkts/b Avg Bwd Blk Rate Avg Subflow Fwd Pkts \\\n", 671 | "0 0 0 0 3 \n", 672 | "1 0 0 0 14 \n", 673 | "2 0 0 0 2 \n", 674 | "3 0 0 0 3 \n", 675 | "4 0 0 0 3 \n", 676 | "\n", 677 | " Subflow Fwd Byts Subflow Bwd Pkts Subflow Bwd Byts Init Fwd Win Byts \\\n", 678 | "0 0 0 0 -1 \n", 679 | "1 2168 12 2993 29200 \n", 680 | "2 64 0 0 244 \n", 681 | "3 0 0 0 -1 \n", 682 | "4 0 0 0 -1 \n", 683 | "\n", 684 | " Init Bwd Win Byts Fwd Act Data Pkts Fwd Seg Size Min Active Mean \\\n", 685 | "0 -1 0 0 0.0 \n", 686 | "1 232 8 32 1024353.0 \n", 687 | "2 -1 0 32 0.0 \n", 688 | "3 -1 0 0 0.0 \n", 689 | "4 -1 0 0 0.0 \n", 690 | "\n", 691 | " Active Std Active Max Active Min Idle Mean Idle Std Idle Max \\\n", 692 | "0 0.000000 0 0 56320579.0 7.042784e+02 56321077 \n", 693 | "1 649038.754495 1601183 321569 11431221.0 3.644991e+06 15617415 \n", 694 | "2 0.000000 0 0 0.0 0.000000e+00 0 \n", 695 | "3 0.000000 0 0 56320351.5 3.669884e+02 56320611 \n", 696 | "4 0.000000 0 0 56320437.0 7.198347e+02 56320946 \n", 697 | "\n", 698 | " Idle Min Label \n", 699 | "0 56320081 Benign \n", 700 | "1 8960247 Benign \n", 701 | "2 0 Benign \n", 702 | "3 56320092 Benign \n", 703 | "4 56319928 Benign " 704 | ] 705 | }, 706 | "execution_count": 6, 707 | "metadata": {}, 708 | "output_type": "execute_result" 709 | } 710 | ], 711 | "source": [ 712 | "data.head()" 713 | ] 714 | }, 715 | { 716 | "cell_type": "code", 717 | "execution_count": 9, 718 | "metadata": {}, 719 | "outputs": [ 720 | { 721 | "data": { 722 | "text/plain": [ 723 | "array([ 0, 6, 17], dtype=int64)" 724 | ] 725 | }, 726 | "execution_count": 9, 727 | "metadata": {}, 728 | "output_type": "execute_result" 729 | } 730 | ], 731 | "source": [ 732 | "data['Protocol'].unique()" 733 | ] 734 | }, 735 | { 736 | "cell_type": "code", 737 | "execution_count": null, 738 | "metadata": {}, 739 | "outputs": [], 740 | "source": [] 741 | } 742 | ], 743 | "metadata": { 744 | "kernelspec": { 745 | "display_name": "Python 3", 746 | "language": "python", 747 | "name": "python3" 748 | }, 749 | "language_info": { 750 | "codemirror_mode": { 751 | "name": "ipython", 752 | "version": 3 753 | }, 754 | "file_extension": ".py", 755 | "mimetype": "text/x-python", 756 | "name": "python", 757 | "nbconvert_exporter": "python", 758 | "pygments_lexer": "ipython3", 759 | "version": "3.7.9" 760 | } 761 | }, 762 | "nbformat": 4, 763 | "nbformat_minor": 4 764 | } 765 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /feature_engineering.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import pandas as pd\n", 11 | "from scipy.stats import pearsonr\n", 12 | "from sklearn.tree import DecisionTreeClassifier\n", 13 | "from sklearn.feature_selection import chi2, mutual_info_classif, RFE, SelectFromModel, SelectKBest, VarianceThreshold\n", 14 | "from sklearn.linear_model import LogisticRegression\n", 15 | "import warnings" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 2, 21 | "metadata": {}, 22 | "outputs": [], 23 | "source": [ 24 | "pd.set_option('display.max_columns', None)\n", 25 | "pd.options.mode.use_inf_as_na = True\n", 26 | "warnings.filterwarnings('ignore')" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 3, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "data = pd.read_csv('G:\\IDS2018\\datasets\\Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv')" 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": {}, 41 | "source": [ 42 | "# 数据预处理" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 4, 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "numerical_fea = list(data.select_dtypes(exclude=['object']).columns)\n", 52 | "category_fea = list(filter(lambda x: x not in numerical_fea, list(data.columns)))" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 5, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [ 61 | "def get_numerical_serial_fea(data, feas):\n", 62 | " numerical_serial_fea = []\n", 63 | " numerical_noserial_fea = []\n", 64 | " for fea in feas:\n", 65 | " temp = data[fea].nunique()\n", 66 | " if temp <= 10:\n", 67 | " numerical_noserial_fea.append(fea)\n", 68 | " else:\n", 69 | " numerical_serial_fea.append(fea)\n", 70 | " return numerical_serial_fea, numerical_noserial_fea\n", 71 | "numerical_serial_fea, numerical_noserial_fea = get_numerical_serial_fea(data, numerical_fea)" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 36, 77 | "metadata": {}, 78 | "outputs": [ 79 | { 80 | "data": { 81 | "text/plain": [ 82 | "{'Dst Port': 0,\n", 83 | " 'Protocol': 0,\n", 84 | " 'Timestamp': 0,\n", 85 | " 'Flow Duration': 0,\n", 86 | " 'Tot Fwd Pkts': 0,\n", 87 | " 'Tot Bwd Pkts': 0,\n", 88 | " 'TotLen Fwd Pkts': 0,\n", 89 | " 'TotLen Bwd Pkts': 0,\n", 90 | " 'Fwd Pkt Len Max': 0,\n", 91 | " 'Fwd Pkt Len Min': 0,\n", 92 | " 'Fwd Pkt Len Mean': 0,\n", 93 | " 'Fwd Pkt Len Std': 0,\n", 94 | " 'Bwd Pkt Len Max': 0,\n", 95 | " 'Bwd Pkt Len Min': 0,\n", 96 | " 'Bwd Pkt Len Mean': 0,\n", 97 | " 'Bwd Pkt Len Std': 0,\n", 98 | " 'Flow Byts/s': 8027,\n", 99 | " 'Flow Pkts/s': 8027,\n", 100 | " 'Flow IAT Mean': 0,\n", 101 | " 'Flow IAT Std': 0,\n", 102 | " 'Flow IAT Max': 0,\n", 103 | " 'Flow IAT Min': 0,\n", 104 | " 'Fwd IAT Tot': 0,\n", 105 | " 'Fwd IAT Mean': 0,\n", 106 | " 'Fwd IAT Std': 0,\n", 107 | " 'Fwd IAT Max': 0,\n", 108 | " 'Fwd IAT Min': 0,\n", 109 | " 'Bwd IAT Tot': 0,\n", 110 | " 'Bwd IAT Mean': 0,\n", 111 | " 'Bwd IAT Std': 0,\n", 112 | " 'Bwd IAT Max': 0,\n", 113 | " 'Bwd IAT Min': 0,\n", 114 | " 'Fwd PSH Flags': 0,\n", 115 | " 'Bwd PSH Flags': 0,\n", 116 | " 'Fwd URG Flags': 0,\n", 117 | " 'Bwd URG Flags': 0,\n", 118 | " 'Fwd Header Len': 0,\n", 119 | " 'Bwd Header Len': 0,\n", 120 | " 'Fwd Pkts/s': 0,\n", 121 | " 'Bwd Pkts/s': 0,\n", 122 | " 'Pkt Len Min': 0,\n", 123 | " 'Pkt Len Max': 0,\n", 124 | " 'Pkt Len Mean': 0,\n", 125 | " 'Pkt Len Std': 0,\n", 126 | " 'Pkt Len Var': 0,\n", 127 | " 'FIN Flag Cnt': 0,\n", 128 | " 'SYN Flag Cnt': 0,\n", 129 | " 'RST Flag Cnt': 0,\n", 130 | " 'PSH Flag Cnt': 0,\n", 131 | " 'ACK Flag Cnt': 0,\n", 132 | " 'URG Flag Cnt': 0,\n", 133 | " 'CWE Flag Count': 0,\n", 134 | " 'ECE Flag Cnt': 0,\n", 135 | " 'Down/Up Ratio': 0,\n", 136 | " 'Pkt Size Avg': 0,\n", 137 | " 'Fwd Seg Size Avg': 0,\n", 138 | " 'Bwd Seg Size Avg': 0,\n", 139 | " 'Fwd Byts/b Avg': 0,\n", 140 | " 'Fwd Pkts/b Avg': 0,\n", 141 | " 'Fwd Blk Rate Avg': 0,\n", 142 | " 'Bwd Byts/b Avg': 0,\n", 143 | " 'Bwd Pkts/b Avg': 0,\n", 144 | " 'Bwd Blk Rate Avg': 0,\n", 145 | " 'Subflow Fwd Pkts': 0,\n", 146 | " 'Subflow Fwd Byts': 0,\n", 147 | " 'Subflow Bwd Pkts': 0,\n", 148 | " 'Subflow Bwd Byts': 0,\n", 149 | " 'Init Fwd Win Byts': 0,\n", 150 | " 'Init Bwd Win Byts': 0,\n", 151 | " 'Fwd Act Data Pkts': 0,\n", 152 | " 'Fwd Seg Size Min': 0,\n", 153 | " 'Active Mean': 0,\n", 154 | " 'Active Std': 0,\n", 155 | " 'Active Max': 0,\n", 156 | " 'Active Min': 0,\n", 157 | " 'Idle Mean': 0,\n", 158 | " 'Idle Std': 0,\n", 159 | " 'Idle Max': 0,\n", 160 | " 'Idle Min': 0,\n", 161 | " 'Label': 0}" 162 | ] 163 | }, 164 | "execution_count": 36, 165 | "metadata": {}, 166 | "output_type": "execute_result" 167 | } 168 | ], 169 | "source": [ 170 | "data.isnull().sum().to_dict()" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 6, 176 | "metadata": {}, 177 | "outputs": [], 178 | "source": [ 179 | "data[numerical_serial_fea] = data[numerical_serial_fea].fillna(data[numerical_serial_fea].mean())" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": 9, 185 | "metadata": {}, 186 | "outputs": [ 187 | { 188 | "data": { 189 | "text/plain": [ 190 | "{'Dst Port': 0,\n", 191 | " 'Protocol': 0,\n", 192 | " 'Timestamp': 0,\n", 193 | " 'Flow Duration': 0,\n", 194 | " 'Tot Fwd Pkts': 0,\n", 195 | " 'Tot Bwd Pkts': 0,\n", 196 | " 'TotLen Fwd Pkts': 0,\n", 197 | " 'TotLen Bwd Pkts': 0,\n", 198 | " 'Fwd Pkt Len Max': 0,\n", 199 | " 'Fwd Pkt Len Min': 0,\n", 200 | " 'Fwd Pkt Len Mean': 0,\n", 201 | " 'Fwd Pkt Len Std': 0,\n", 202 | " 'Bwd Pkt Len Max': 0,\n", 203 | " 'Bwd Pkt Len Min': 0,\n", 204 | " 'Bwd Pkt Len Mean': 0,\n", 205 | " 'Bwd Pkt Len Std': 0,\n", 206 | " 'Flow Byts/s': 0,\n", 207 | " 'Flow Pkts/s': 0,\n", 208 | " 'Flow IAT Mean': 0,\n", 209 | " 'Flow IAT Std': 0,\n", 210 | " 'Flow IAT Max': 0,\n", 211 | " 'Flow IAT Min': 0,\n", 212 | " 'Fwd IAT Tot': 0,\n", 213 | " 'Fwd IAT Mean': 0,\n", 214 | " 'Fwd IAT Std': 0,\n", 215 | " 'Fwd IAT Max': 0,\n", 216 | " 'Fwd IAT Min': 0,\n", 217 | " 'Bwd IAT Tot': 0,\n", 218 | " 'Bwd IAT Mean': 0,\n", 219 | " 'Bwd IAT Std': 0,\n", 220 | " 'Bwd IAT Max': 0,\n", 221 | " 'Bwd IAT Min': 0,\n", 222 | " 'Fwd PSH Flags': 0,\n", 223 | " 'Bwd PSH Flags': 0,\n", 224 | " 'Fwd URG Flags': 0,\n", 225 | " 'Bwd URG Flags': 0,\n", 226 | " 'Fwd Header Len': 0,\n", 227 | " 'Bwd Header Len': 0,\n", 228 | " 'Fwd Pkts/s': 0,\n", 229 | " 'Bwd Pkts/s': 0,\n", 230 | " 'Pkt Len Min': 0,\n", 231 | " 'Pkt Len Max': 0,\n", 232 | " 'Pkt Len Mean': 0,\n", 233 | " 'Pkt Len Std': 0,\n", 234 | " 'Pkt Len Var': 0,\n", 235 | " 'FIN Flag Cnt': 0,\n", 236 | " 'SYN Flag Cnt': 0,\n", 237 | " 'RST Flag Cnt': 0,\n", 238 | " 'PSH Flag Cnt': 0,\n", 239 | " 'ACK Flag Cnt': 0,\n", 240 | " 'URG Flag Cnt': 0,\n", 241 | " 'CWE Flag Count': 0,\n", 242 | " 'ECE Flag Cnt': 0,\n", 243 | " 'Down/Up Ratio': 0,\n", 244 | " 'Pkt Size Avg': 0,\n", 245 | " 'Fwd Seg Size Avg': 0,\n", 246 | " 'Bwd Seg Size Avg': 0,\n", 247 | " 'Fwd Byts/b Avg': 0,\n", 248 | " 'Fwd Pkts/b Avg': 0,\n", 249 | " 'Fwd Blk Rate Avg': 0,\n", 250 | " 'Bwd Byts/b Avg': 0,\n", 251 | " 'Bwd Pkts/b Avg': 0,\n", 252 | " 'Bwd Blk Rate Avg': 0,\n", 253 | " 'Subflow Fwd Pkts': 0,\n", 254 | " 'Subflow Fwd Byts': 0,\n", 255 | " 'Subflow Bwd Pkts': 0,\n", 256 | " 'Subflow Bwd Byts': 0,\n", 257 | " 'Init Fwd Win Byts': 0,\n", 258 | " 'Init Bwd Win Byts': 0,\n", 259 | " 'Fwd Act Data Pkts': 0,\n", 260 | " 'Fwd Seg Size Min': 0,\n", 261 | " 'Active Mean': 0,\n", 262 | " 'Active Std': 0,\n", 263 | " 'Active Max': 0,\n", 264 | " 'Active Min': 0,\n", 265 | " 'Idle Mean': 0,\n", 266 | " 'Idle Std': 0,\n", 267 | " 'Idle Max': 0,\n", 268 | " 'Idle Min': 0,\n", 269 | " 'Label': 0}" 270 | ] 271 | }, 272 | "execution_count": 9, 273 | "metadata": {}, 274 | "output_type": "execute_result" 275 | } 276 | ], 277 | "source": [ 278 | "data.isnull().sum().to_dict()" 279 | ] 280 | }, 281 | { 282 | "cell_type": "code", 283 | "execution_count": 7, 284 | "metadata": {}, 285 | "outputs": [], 286 | "source": [ 287 | "data['Timestamp'] = pd.to_datetime(data['Timestamp'],format='%d/%m/%Y %H:%M:%S')" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 7, 293 | "metadata": {}, 294 | "outputs": [], 295 | "source": [ 296 | "data['Label'].replace(to_replace='Benign', value=0, inplace=True)\n", 297 | "data['Label'].replace(to_replace='DoS attacks-GoldenEye', value=1, inplace=True)\n", 298 | "data['Label'].replace(to_replace='DoS attacks-Slowloris', value=1, inplace=True)" 299 | ] 300 | }, 301 | { 302 | "cell_type": "markdown", 303 | "metadata": {}, 304 | "source": [ 305 | "# 异常值处理" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": 10, 311 | "metadata": {}, 312 | "outputs": [], 313 | "source": [ 314 | "def find_outliers_by_3segama(data, fea):\n", 315 | " data_std = np.std(data[fea])\n", 316 | " data_mean = np.mean(data[fea])\n", 317 | " outliers_cut_off = 3 * data_std\n", 318 | " lower_rule = data_mean - outliers_cut_off\n", 319 | " upper_rule = data_mean + outliers_cut_off\n", 320 | " data[fea+'_outliers'] = data[fea].apply(lambda x: str('异常值') if x > upper_rule or x < lower_rule else '正常值')\n", 321 | " return data" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": 27, 327 | "metadata": {}, 328 | "outputs": [ 329 | { 330 | "name": "stdout", 331 | "output_type": "stream", 332 | "text": [ 333 | "正常值 1039789\n", 334 | "异常值 8786\n", 335 | "Name: Dst Port_outliers, dtype: int64\n", 336 | "Dst Port_outliers\n", 337 | "异常值 0\n", 338 | "正常值 52498\n", 339 | "Name: Label, dtype: int64\n", 340 | "**********\n", 341 | "正常值 991764\n", 342 | "异常值 56811\n", 343 | "Name: Flow Duration_outliers, dtype: int64\n", 344 | "Flow Duration_outliers\n", 345 | "异常值 280\n", 346 | "正常值 52218\n", 347 | "Name: Label, dtype: int64\n", 348 | "**********\n", 349 | "正常值 1046745\n", 350 | "异常值 1830\n", 351 | "Name: Tot Fwd Pkts_outliers, dtype: int64\n", 352 | "Tot Fwd Pkts_outliers\n", 353 | "异常值 0\n", 354 | "正常值 52498\n", 355 | "Name: Label, dtype: int64\n", 356 | "**********\n", 357 | "正常值 1046952\n", 358 | "异常值 1623\n", 359 | "Name: Tot Bwd Pkts_outliers, dtype: int64\n", 360 | "Tot Bwd Pkts_outliers\n", 361 | "异常值 0\n", 362 | "正常值 52498\n", 363 | "Name: Label, dtype: int64\n", 364 | "**********\n", 365 | "正常值 1048549\n", 366 | "异常值 26\n", 367 | "Name: TotLen Fwd Pkts_outliers, dtype: int64\n", 368 | "TotLen Fwd Pkts_outliers\n", 369 | "异常值 0\n", 370 | "正常值 52498\n", 371 | "Name: Label, dtype: int64\n", 372 | "**********\n", 373 | "正常值 1047070\n", 374 | "异常值 1505\n", 375 | "Name: TotLen Bwd Pkts_outliers, dtype: int64\n", 376 | "TotLen Bwd Pkts_outliers\n", 377 | "异常值 0\n", 378 | "正常值 52498\n", 379 | "Name: Label, dtype: int64\n", 380 | "**********\n", 381 | "正常值 1038021\n", 382 | "异常值 10554\n", 383 | "Name: Fwd Pkt Len Max_outliers, dtype: int64\n", 384 | "Fwd Pkt Len Max_outliers\n", 385 | "异常值 0\n", 386 | "正常值 52498\n", 387 | "Name: Label, dtype: int64\n", 388 | "**********\n", 389 | "正常值 1045594\n", 390 | "异常值 2981\n", 391 | "Name: Fwd Pkt Len Min_outliers, dtype: int64\n", 392 | "Fwd Pkt Len Min_outliers\n", 393 | "异常值 98\n", 394 | "正常值 52400\n", 395 | "Name: Label, dtype: int64\n", 396 | "**********\n", 397 | "正常值 1039579\n", 398 | "异常值 8996\n", 399 | "Name: Fwd Pkt Len Mean_outliers, dtype: int64\n", 400 | "Fwd Pkt Len Mean_outliers\n", 401 | "异常值 1178\n", 402 | "正常值 51320\n", 403 | "Name: Label, dtype: int64\n", 404 | "**********\n", 405 | "正常值 1037266\n", 406 | "异常值 11309\n", 407 | "Name: Fwd Pkt Len Std_outliers, dtype: int64\n", 408 | "Fwd Pkt Len Std_outliers\n", 409 | "异常值 634\n", 410 | "正常值 51864\n", 411 | "Name: Label, dtype: int64\n", 412 | "**********\n", 413 | "正常值 1048568\n", 414 | "异常值 7\n", 415 | "Name: Bwd Pkt Len Max_outliers, dtype: int64\n", 416 | "Bwd Pkt Len Max_outliers\n", 417 | "异常值 0\n", 418 | "正常值 52498\n", 419 | "Name: Label, dtype: int64\n", 420 | "**********\n", 421 | "正常值 1033705\n", 422 | "异常值 14870\n", 423 | "Name: Bwd Pkt Len Min_outliers, dtype: int64\n", 424 | "Bwd Pkt Len Min_outliers\n", 425 | "异常值 0\n", 426 | "正常值 52498\n", 427 | "Name: Label, dtype: int64\n", 428 | "**********\n", 429 | "正常值 1022827\n", 430 | "异常值 25748\n", 431 | "Name: Bwd Pkt Len Mean_outliers, dtype: int64\n", 432 | "Bwd Pkt Len Mean_outliers\n", 433 | "异常值 0\n", 434 | "正常值 52498\n", 435 | "Name: Label, dtype: int64\n", 436 | "**********\n", 437 | "正常值 1047631\n", 438 | "异常值 944\n", 439 | "Name: Bwd Pkt Len Std_outliers, dtype: int64\n", 440 | "Bwd Pkt Len Std_outliers\n", 441 | "异常值 0\n", 442 | "正常值 52498\n", 443 | "Name: Label, dtype: int64\n", 444 | "**********\n", 445 | "正常值 1043268\n", 446 | "异常值 5307\n", 447 | "Name: Flow Byts/s_outliers, dtype: int64\n", 448 | "Flow Byts/s_outliers\n", 449 | "异常值 4\n", 450 | "正常值 52494\n", 451 | "Name: Label, dtype: int64\n", 452 | "**********\n", 453 | "正常值 1031972\n", 454 | "异常值 16603\n", 455 | "Name: Flow Pkts/s_outliers, dtype: int64\n", 456 | "Flow Pkts/s_outliers\n", 457 | "异常值 458\n", 458 | "正常值 52040\n", 459 | "Name: Label, dtype: int64\n", 460 | "**********\n", 461 | "正常值 1004984\n", 462 | "异常值 43591\n", 463 | "Name: Flow IAT Mean_outliers, dtype: int64\n", 464 | "Flow IAT Mean_outliers\n", 465 | "异常值 3115\n", 466 | "正常值 49383\n", 467 | "Name: Label, dtype: int64\n", 468 | "**********\n", 469 | "正常值 1033696\n", 470 | "异常值 14879\n", 471 | "Name: Flow IAT Std_outliers, dtype: int64\n", 472 | "Flow IAT Std_outliers\n", 473 | "异常值 3698\n", 474 | "正常值 48800\n", 475 | "Name: Label, dtype: int64\n", 476 | "**********\n", 477 | "正常值 1021705\n", 478 | "异常值 26870\n", 479 | "Name: Flow IAT Max_outliers, dtype: int64\n", 480 | "Flow IAT Max_outliers\n", 481 | "异常值 3778\n", 482 | "正常值 48720\n", 483 | "Name: Label, dtype: int64\n", 484 | "**********\n", 485 | "正常值 1006778\n", 486 | "异常值 41797\n", 487 | "Name: Flow IAT Min_outliers, dtype: int64\n", 488 | "Flow IAT Min_outliers\n", 489 | "异常值 1862\n", 490 | "正常值 50636\n", 491 | "Name: Label, dtype: int64\n", 492 | "**********\n", 493 | "正常值 991938\n", 494 | "异常值 56637\n", 495 | "Name: Fwd IAT Tot_outliers, dtype: int64\n", 496 | "Fwd IAT Tot_outliers\n", 497 | "异常值 281\n", 498 | "正常值 52217\n", 499 | "Name: Label, dtype: int64\n", 500 | "**********\n", 501 | "正常值 1002872\n", 502 | "异常值 45703\n", 503 | "Name: Fwd IAT Mean_outliers, dtype: int64\n", 504 | "Fwd IAT Mean_outliers\n", 505 | "异常值 4781\n", 506 | "正常值 47717\n", 507 | "Name: Label, dtype: int64\n", 508 | "**********\n", 509 | "正常值 1010889\n", 510 | "异常值 37686\n", 511 | "Name: Fwd IAT Std_outliers, dtype: int64\n", 512 | "Fwd IAT Std_outliers\n", 513 | "异常值 239\n", 514 | "正常值 52259\n", 515 | "Name: Label, dtype: int64\n", 516 | "**********\n", 517 | "正常值 1004804\n", 518 | "异常值 43771\n", 519 | "Name: Fwd IAT Max_outliers, dtype: int64\n", 520 | "Fwd IAT Max_outliers\n", 521 | "异常值 3778\n", 522 | "正常值 48720\n", 523 | "Name: Label, dtype: int64\n", 524 | "**********\n", 525 | "正常值 1002826\n", 526 | "异常值 45749\n", 527 | "Name: Fwd IAT Min_outliers, dtype: int64\n", 528 | "Fwd IAT Min_outliers\n", 529 | "异常值 4902\n", 530 | "正常值 47596\n", 531 | "Name: Label, dtype: int64\n", 532 | "**********\n", 533 | "正常值 979526\n", 534 | "异常值 69049\n", 535 | "Name: Bwd IAT Tot_outliers, dtype: int64\n", 536 | "Bwd IAT Tot_outliers\n", 537 | "异常值 7142\n", 538 | "正常值 45356\n", 539 | "Name: Label, dtype: int64\n", 540 | "**********\n", 541 | "正常值 1035909\n", 542 | "异常值 12666\n", 543 | "Name: Bwd IAT Mean_outliers, dtype: int64\n", 544 | "Bwd IAT Mean_outliers\n", 545 | "异常值 7353\n", 546 | "正常值 45145\n", 547 | "Name: Label, dtype: int64\n", 548 | "**********\n", 549 | "正常值 1019297\n", 550 | "异常值 29278\n", 551 | "Name: Bwd IAT Std_outliers, dtype: int64\n", 552 | "Bwd IAT Std_outliers\n", 553 | "异常值 5001\n", 554 | "正常值 47497\n", 555 | "Name: Label, dtype: int64\n", 556 | "**********\n", 557 | "正常值 1007151\n", 558 | "异常值 41424\n", 559 | "Name: Bwd IAT Max_outliers, dtype: int64\n", 560 | "Bwd IAT Max_outliers\n", 561 | "异常值 7196\n", 562 | "正常值 45302\n", 563 | "Name: Label, dtype: int64\n", 564 | "**********\n", 565 | "正常值 1037488\n", 566 | "异常值 11087\n", 567 | "Name: Bwd IAT Min_outliers, dtype: int64\n", 568 | "Bwd IAT Min_outliers\n", 569 | "异常值 7150\n", 570 | "正常值 45348\n", 571 | "Name: Label, dtype: int64\n", 572 | "**********\n", 573 | "正常值 1046973\n", 574 | "异常值 1602\n", 575 | "Name: Fwd Header Len_outliers, dtype: int64\n", 576 | "Fwd Header Len_outliers\n", 577 | "异常值 0\n", 578 | "正常值 52498\n", 579 | "Name: Label, dtype: int64\n", 580 | "**********\n", 581 | "正常值 1046933\n", 582 | "异常值 1642\n", 583 | "Name: Bwd Header Len_outliers, dtype: int64\n", 584 | "Bwd Header Len_outliers\n", 585 | "异常值 0\n", 586 | "正常值 52498\n", 587 | "Name: Label, dtype: int64\n", 588 | "**********\n", 589 | "正常值 1032449\n", 590 | "异常值 16126\n", 591 | "Name: Fwd Pkts/s_outliers, dtype: int64\n", 592 | "Fwd Pkts/s_outliers\n", 593 | "异常值 55\n", 594 | "正常值 52443\n", 595 | "Name: Label, dtype: int64\n", 596 | "**********\n", 597 | "正常值 1046807\n", 598 | "异常值 1768\n", 599 | "Name: Bwd Pkts/s_outliers, dtype: int64\n", 600 | "Bwd Pkts/s_outliers\n", 601 | "异常值 754\n", 602 | "正常值 51744\n", 603 | "Name: Label, dtype: int64\n", 604 | "**********\n", 605 | "正常值 1044348\n", 606 | "异常值 4227\n", 607 | "Name: Pkt Len Min_outliers, dtype: int64\n", 608 | "Pkt Len Min_outliers\n", 609 | "异常值 0\n", 610 | "正常值 52498\n", 611 | "Name: Label, dtype: int64\n", 612 | "**********\n", 613 | "正常值 1048557\n", 614 | "异常值 18\n", 615 | "Name: Pkt Len Max_outliers, dtype: int64\n", 616 | "Pkt Len Max_outliers\n", 617 | "异常值 0\n", 618 | "正常值 52498\n", 619 | "Name: Label, dtype: int64\n", 620 | "**********\n", 621 | "正常值 1029538\n", 622 | "异常值 19037\n", 623 | "Name: Pkt Len Mean_outliers, dtype: int64\n", 624 | "Pkt Len Mean_outliers\n", 625 | "异常值 0\n", 626 | "正常值 52498\n", 627 | "Name: Label, dtype: int64\n", 628 | "**********\n", 629 | "正常值 1029158\n", 630 | "异常值 19417\n", 631 | "Name: Pkt Len Std_outliers, dtype: int64\n", 632 | "Pkt Len Std_outliers\n", 633 | "异常值 0\n", 634 | "正常值 52498\n", 635 | "Name: Label, dtype: int64\n", 636 | "**********\n", 637 | "正常值 1048554\n", 638 | "异常值 21\n", 639 | "Name: Pkt Len Var_outliers, dtype: int64\n", 640 | "Pkt Len Var_outliers\n", 641 | "异常值 0\n", 642 | "正常值 52498\n", 643 | "Name: Label, dtype: int64\n", 644 | "**********\n", 645 | "正常值 1044334\n", 646 | "异常值 4241\n", 647 | "Name: Down/Up Ratio_outliers, dtype: int64\n", 648 | "Down/Up Ratio_outliers\n", 649 | "异常值 0\n", 650 | "正常值 52498\n", 651 | "Name: Label, dtype: int64\n", 652 | "**********\n", 653 | "正常值 1028615\n", 654 | "异常值 19960\n", 655 | "Name: Pkt Size Avg_outliers, dtype: int64\n", 656 | "Pkt Size Avg_outliers\n", 657 | "异常值 2\n", 658 | "正常值 52496\n", 659 | "Name: Label, dtype: int64\n", 660 | "**********\n", 661 | "正常值 1039579\n", 662 | "异常值 8996\n", 663 | "Name: Fwd Seg Size Avg_outliers, dtype: int64\n", 664 | "Fwd Seg Size Avg_outliers\n", 665 | "异常值 1178\n", 666 | "正常值 51320\n", 667 | "Name: Label, dtype: int64\n", 668 | "**********\n", 669 | "正常值 1022827\n", 670 | "异常值 25748\n", 671 | "Name: Bwd Seg Size Avg_outliers, dtype: int64\n", 672 | "Bwd Seg Size Avg_outliers\n", 673 | "异常值 0\n", 674 | "正常值 52498\n", 675 | "Name: Label, dtype: int64\n", 676 | "**********\n", 677 | "正常值 1046745\n", 678 | "异常值 1830\n", 679 | "Name: Subflow Fwd Pkts_outliers, dtype: int64\n", 680 | "Subflow Fwd Pkts_outliers\n", 681 | "异常值 0\n", 682 | "正常值 52498\n", 683 | "Name: Label, dtype: int64\n", 684 | "**********\n", 685 | "正常值 1048549\n", 686 | "异常值 26\n", 687 | "Name: Subflow Fwd Byts_outliers, dtype: int64\n", 688 | "Subflow Fwd Byts_outliers\n", 689 | "异常值 0\n", 690 | "正常值 52498\n", 691 | "Name: Label, dtype: int64\n", 692 | "**********\n", 693 | "正常值 1046952\n", 694 | "异常值 1623\n", 695 | "Name: Subflow Bwd Pkts_outliers, dtype: int64\n", 696 | "Subflow Bwd Pkts_outliers\n", 697 | "异常值 0\n", 698 | "正常值 52498\n", 699 | "Name: Label, dtype: int64\n", 700 | "**********\n", 701 | "正常值 1047070\n", 702 | "异常值 1505\n", 703 | "Name: Subflow Bwd Byts_outliers, dtype: int64\n", 704 | "Subflow Bwd Byts_outliers\n", 705 | "异常值 0\n", 706 | "正常值 52498\n", 707 | "Name: Label, dtype: int64\n", 708 | "**********\n", 709 | "正常值 1025928\n", 710 | "异常值 22647\n", 711 | "Name: Init Fwd Win Byts_outliers, dtype: int64\n", 712 | "Init Fwd Win Byts_outliers\n", 713 | "异常值 0\n", 714 | "正常值 52498\n", 715 | "Name: Label, dtype: int64\n", 716 | "**********\n", 717 | "正常值 966856\n", 718 | "异常值 81719\n", 719 | "Name: Init Bwd Win Byts_outliers, dtype: int64\n", 720 | "Init Bwd Win Byts_outliers\n", 721 | "异常值 0\n", 722 | "正常值 52498\n", 723 | "Name: Label, dtype: int64\n", 724 | "**********\n", 725 | "正常值 1044073\n", 726 | "异常值 4502\n", 727 | "Name: Fwd Act Data Pkts_outliers, dtype: int64\n", 728 | "Fwd Act Data Pkts_outliers\n", 729 | "异常值 0\n", 730 | "正常值 52498\n", 731 | "Name: Label, dtype: int64\n", 732 | "**********\n", 733 | "正常值 1033732\n", 734 | "异常值 14843\n", 735 | "Name: Fwd Seg Size Min_outliers, dtype: int64\n", 736 | "Fwd Seg Size Min_outliers\n", 737 | "异常值 2232\n", 738 | "正常值 50266\n", 739 | "Name: Label, dtype: int64\n", 740 | "**********\n", 741 | "正常值 1034611\n", 742 | "异常值 13964\n", 743 | "Name: Active Mean_outliers, dtype: int64\n", 744 | "Active Mean_outliers\n", 745 | "异常值 5598\n", 746 | "正常值 46900\n", 747 | "Name: Label, dtype: int64\n", 748 | "**********\n" 749 | ] 750 | }, 751 | { 752 | "name": "stdout", 753 | "output_type": "stream", 754 | "text": [ 755 | "正常值 1034351\n", 756 | "异常值 14224\n", 757 | "Name: Active Std_outliers, dtype: int64\n", 758 | "Active Std_outliers\n", 759 | "异常值 4935\n", 760 | "正常值 47563\n", 761 | "Name: Label, dtype: int64\n", 762 | "**********\n", 763 | "正常值 1032734\n", 764 | "异常值 15841\n", 765 | "Name: Active Max_outliers, dtype: int64\n", 766 | "Active Max_outliers\n", 767 | "异常值 5557\n", 768 | "正常值 46941\n", 769 | "Name: Label, dtype: int64\n", 770 | "**********\n", 771 | "正常值 1035242\n", 772 | "异常值 13333\n", 773 | "Name: Active Min_outliers, dtype: int64\n", 774 | "Active Min_outliers\n", 775 | "异常值 5463\n", 776 | "正常值 47035\n", 777 | "Name: Label, dtype: int64\n", 778 | "**********\n", 779 | "正常值 996157\n", 780 | "异常值 52418\n", 781 | "Name: Idle Mean_outliers, dtype: int64\n", 782 | "Idle Mean_outliers\n", 783 | "异常值 4229\n", 784 | "正常值 48269\n", 785 | "Name: Label, dtype: int64\n", 786 | "**********\n", 787 | "正常值 1037445\n", 788 | "异常值 11130\n", 789 | "Name: Idle Std_outliers, dtype: int64\n", 790 | "Idle Std_outliers\n", 791 | "异常值 4788\n", 792 | "正常值 47710\n", 793 | "Name: Label, dtype: int64\n", 794 | "**********\n", 795 | "正常值 988121\n", 796 | "异常值 60454\n", 797 | "Name: Idle Max_outliers, dtype: int64\n", 798 | "Idle Max_outliers\n", 799 | "异常值 8686\n", 800 | "正常值 43812\n", 801 | "Name: Label, dtype: int64\n", 802 | "**********\n", 803 | "正常值 996490\n", 804 | "异常值 52085\n", 805 | "Name: Idle Min_outliers, dtype: int64\n", 806 | "Idle Min_outliers\n", 807 | "异常值 4277\n", 808 | "正常值 48221\n", 809 | "Name: Label, dtype: int64\n", 810 | "**********\n" 811 | ] 812 | } 813 | ], 814 | "source": [ 815 | "for fea in numerical_serial_fea:\n", 816 | " data = find_outliers_by_3segama(data, fea)\n", 817 | " print(data[fea+'_outliers'].value_counts())\n", 818 | " print(data.groupby(fea+'_outliers')['Label'].sum())\n", 819 | " print('*'*10)" 820 | ] 821 | }, 822 | { 823 | "cell_type": "markdown", 824 | "metadata": {}, 825 | "source": [ 826 | "# 特征选择" 827 | ] 828 | }, 829 | { 830 | "cell_type": "code", 831 | "execution_count": 47, 832 | "metadata": {}, 833 | "outputs": [ 834 | { 835 | "name": "stdout", 836 | "output_type": "stream", 837 | "text": [ 838 | "Selected: ['Dst Port' 'Flow Duration' 'Tot Fwd Pkts' 'Tot Bwd Pkts'\n", 839 | " 'TotLen Fwd Pkts' 'TotLen Bwd Pkts' 'Fwd Pkt Len Max' 'Fwd Pkt Len Min'\n", 840 | " 'Fwd Pkt Len Mean' 'Fwd Pkt Len Std' 'Bwd Pkt Len Max' 'Bwd Pkt Len Min'\n", 841 | " 'Bwd Pkt Len Mean' 'Bwd Pkt Len Std' 'Flow Byts/s' 'Flow Pkts/s'\n", 842 | " 'Flow IAT Mean' 'Flow IAT Std' 'Flow IAT Max' 'Flow IAT Min'\n", 843 | " 'Fwd IAT Tot' 'Fwd IAT Mean' 'Fwd IAT Std' 'Fwd IAT Max' 'Fwd IAT Min'\n", 844 | " 'Bwd IAT Tot' 'Bwd IAT Mean' 'Bwd IAT Std' 'Bwd IAT Max' 'Bwd IAT Min'\n", 845 | " 'Fwd Header Len' 'Bwd Header Len' 'Fwd Pkts/s' 'Bwd Pkts/s' 'Pkt Len Min'\n", 846 | " 'Pkt Len Max' 'Pkt Len Mean' 'Pkt Len Std' 'Pkt Len Var' 'Pkt Size Avg'\n", 847 | " 'Fwd Seg Size Avg' 'Bwd Seg Size Avg' 'Subflow Fwd Pkts'\n", 848 | " 'Subflow Fwd Byts' 'Subflow Bwd Pkts' 'Subflow Bwd Byts'\n", 849 | " 'Init Fwd Win Byts' 'Init Bwd Win Byts' 'Fwd Act Data Pkts'\n", 850 | " 'Fwd Seg Size Min' 'Active Mean' 'Active Std' 'Active Max' 'Active Min'\n", 851 | " 'Idle Mean' 'Idle Std' 'Idle Max' 'Idle Min']\n", 852 | "Deleted: ['Down/Up Ratio']\n" 853 | ] 854 | } 855 | ], 856 | "source": [ 857 | "selector = VarianceThreshold(threshold=3)\n", 858 | "selector = selector.fit(data[numerical_serial_fea])\n", 859 | "features_mask = selector.get_support(indices=True)\n", 860 | "selected_features = np.array(numerical_serial_fea)[features_mask]\n", 861 | "print('Selected:', selected_features)\n", 862 | "print('Deleted: ', [fea for fea in numerical_serial_fea if fea not in selected_features])" 863 | ] 864 | }, 865 | { 866 | "cell_type": "code", 867 | "execution_count": 12, 868 | "metadata": {}, 869 | "outputs": [ 870 | { 871 | "data": { 872 | "text/plain": [ 873 | "[('Fwd Seg Size Min', (0.4827216248545034, 0.0)),\n", 874 | " ('Bwd IAT Mean', (0.30192251129739955, 0.0)),\n", 875 | " ('Init Fwd Win Byts', (0.2684559690822708, 0.0)),\n", 876 | " ('Bwd IAT Min', (0.2500177147030524, 0.0)),\n", 877 | " ('Flow IAT Std', (0.21858340497356774, 0.0)),\n", 878 | " ('Bwd IAT Max', (0.19606408350148533, 0.0)),\n", 879 | " ('Bwd IAT Std', (0.18719441138249784, 0.0)),\n", 880 | " ('Idle Max', (0.18100280839192964, 0.0)),\n", 881 | " ('Fwd Pkt Len Std', (0.16886516326958462, 0.0)),\n", 882 | " ('Idle Std', (0.15481787043716266, 0.0))]" 883 | ] 884 | }, 885 | "execution_count": 12, 886 | "metadata": {}, 887 | "output_type": "execute_result" 888 | } 889 | ], 890 | "source": [ 891 | "pearsonr_result = []\n", 892 | "for fea in numerical_serial_fea:\n", 893 | " pearsonr_result.append((fea, pearsonr(data[fea], data['Label'])))\n", 894 | "sorted(pearsonr_result, key=lambda x: abs(x[1][0]), reverse=True)[:10]" 895 | ] 896 | }, 897 | { 898 | "cell_type": "code", 899 | "execution_count": 21, 900 | "metadata": {}, 901 | "outputs": [ 902 | { 903 | "name": "stdout", 904 | "output_type": "stream", 905 | "text": [ 906 | "Dst Port\n", 907 | "(array([3.99751145e+08]), array([0.]))\n", 908 | "Flow Duration\n", 909 | "(array([3.59172003e+11]), array([0.]))\n", 910 | "Tot Fwd Pkts\n", 911 | "(array([3778.26711379]), array([0.]))\n", 912 | "Tot Bwd Pkts\n", 913 | "(array([147047.64832782]), array([0.]))\n", 914 | "TotLen Fwd Pkts\n", 915 | "(array([634718.39574114]), array([0.]))\n", 916 | "TotLen Bwd Pkts\n", 917 | "(array([2.24665013e+08]), array([0.]))\n", 918 | "Fwd Pkt Len Max\n", 919 | "(array([3126399.65719183]), array([0.]))\n", 920 | "Fwd Pkt Len Min\n", 921 | "(array([672290.30465466]), array([0.]))\n", 922 | "Fwd Pkt Len Mean\n", 923 | "(array([1008178.14621648]), array([0.]))\n", 924 | "Fwd Pkt Len Std\n", 925 | "(array([4910010.25013736]), array([0.]))\n", 926 | "Bwd Pkt Len Max\n", 927 | "(array([2426276.39917122]), array([0.]))\n", 928 | "Bwd Pkt Len Min\n", 929 | "(array([1859688.16916162]), array([0.]))\n", 930 | "Bwd Pkt Len Mean\n", 931 | "(array([14682.06813255]), array([0.]))\n", 932 | "Bwd Pkt Len Std\n", 933 | "(array([6324182.09067248]), array([0.]))\n", 934 | "Flow Byts/s\n", 935 | "(array([1.5768512e+10]), array([0.]))\n", 936 | "Flow Pkts/s\n", 937 | "(array([1.00582909e+09]), array([0.]))\n", 938 | "Flow IAT Mean\n", 939 | "(array([2.96727475e+11]), array([0.]))\n", 940 | "Flow IAT Std\n", 941 | "(array([9.98169939e+11]), array([0.]))\n", 942 | "Flow IAT Max\n", 943 | "(array([1.02174605e+12]), array([0.]))\n", 944 | "Flow IAT Min\n", 945 | "(array([2.28890909e+10]), array([0.]))\n", 946 | "Fwd IAT Tot\n", 947 | "(array([2.86848641e+11]), array([0.]))\n", 948 | "Fwd IAT Mean\n", 949 | "(array([9.49019226e+11]), array([0.]))\n", 950 | "Fwd IAT Std\n", 951 | "(array([4.93833027e+10]), array([0.]))\n", 952 | "Fwd IAT Max\n", 953 | "(array([9.7532279e+11]), array([0.]))\n", 954 | "Fwd IAT Min\n", 955 | "(array([1.06993311e+12]), array([0.]))\n", 956 | "Bwd IAT Tot\n", 957 | "(array([3.67104733e+11]), array([0.]))\n", 958 | "Bwd IAT Mean\n", 959 | "(array([3.57822439e+12]), array([0.]))\n", 960 | "Bwd IAT Std\n", 961 | "(array([5.31186971e+11]), array([0.]))\n", 962 | "Bwd IAT Max\n", 963 | "(array([1.79577127e+12]), array([0.]))\n", 964 | "Bwd IAT Min\n", 965 | "(array([4.16347887e+12]), array([0.]))\n", 966 | "Fwd Header Len\n", 967 | "(array([1134722.69775687]), array([0.]))\n", 968 | "Bwd Header Len\n", 969 | "(array([1334952.53878541]), array([0.]))\n", 970 | "Fwd Pkts/s\n", 971 | "(array([1.3340166e+09]), array([0.]))\n", 972 | "Bwd Pkts/s\n", 973 | "(array([1.27108414e+08]), array([0.]))\n", 974 | "Pkt Len Min\n", 975 | "(array([763088.03979813]), array([0.]))\n", 976 | "Pkt Len Max\n", 977 | "(array([3235866.47097282]), array([0.]))\n", 978 | "Pkt Len Mean\n", 979 | "(array([79343.84160227]), array([0.]))\n", 980 | "Pkt Len Std\n", 981 | "(array([2061704.30901782]), array([0.]))\n", 982 | "Pkt Len Var\n", 983 | "(array([2.48367375e+08]), array([0.]))\n", 984 | "Down/Up Ratio\n", 985 | "(array([3734.48793754]), array([0.]))\n", 986 | "Pkt Size Avg\n", 987 | "(array([40190.97203183]), array([0.]))\n", 988 | "Fwd Seg Size Avg\n", 989 | "(array([1008178.14621648]), array([0.]))\n", 990 | "Bwd Seg Size Avg\n", 991 | "(array([14682.06813255]), array([0.]))\n", 992 | "Subflow Fwd Pkts\n", 993 | "(array([3778.26711379]), array([0.]))\n", 994 | "Subflow Fwd Byts\n", 995 | "(array([634718.39574114]), array([0.]))\n", 996 | "Subflow Bwd Pkts\n", 997 | "(array([147047.64832782]), array([0.]))\n", 998 | "Subflow Bwd Byts\n", 999 | "(array([2.24665013e+08]), array([0.]))\n", 1000 | "Fwd Act Data Pkts\n", 1001 | "(array([870.91484755]), array([2.06394786e-191]))\n", 1002 | "Fwd Seg Size Min\n", 1003 | "(array([823283.21674079]), array([0.]))\n", 1004 | "Active Mean\n", 1005 | "(array([1.42494306e+11]), array([0.]))\n", 1006 | "Active Std\n", 1007 | "(array([3.00722196e+10]), array([0.]))\n", 1008 | "Active Max\n", 1009 | "(array([1.15913032e+11]), array([0.]))\n", 1010 | "Active Min\n", 1011 | "(array([1.15899376e+11]), array([0.]))\n", 1012 | "Idle Mean\n", 1013 | "(array([1.11834743e+12]), array([0.]))\n", 1014 | "Idle Std\n", 1015 | "(array([5.2196894e+11]), array([0.]))\n", 1016 | "Idle Max\n", 1017 | "(array([1.64438117e+12]), array([0.]))\n", 1018 | "Idle Min\n", 1019 | "(array([8.75959539e+11]), array([0.]))\n" 1020 | ] 1021 | } 1022 | ], 1023 | "source": [ 1024 | "# find out the features can not be applied by chi2\n", 1025 | "for fea in [fea for fea in numerical_serial_fea if fea not in ['Init Fwd Win Byts', 'Init Bwd Win Byts']]:\n", 1026 | " print(fea)\n", 1027 | " print(chi2(np.array(data[fea]).reshape(-1, 1), np.array(data['Label']).reshape(-1, 1)))" 1028 | ] 1029 | }, 1030 | { 1031 | "cell_type": "code", 1032 | "execution_count": 49, 1033 | "metadata": {}, 1034 | "outputs": [ 1035 | { 1036 | "name": "stdout", 1037 | "output_type": "stream", 1038 | "text": [ 1039 | "Selected: ['Bwd IAT Mean' 'Bwd IAT Max' 'Bwd IAT Min' 'Idle Mean' 'Idle Max']\n" 1040 | ] 1041 | } 1042 | ], 1043 | "source": [ 1044 | "chi2_test_fea = [fea for fea in numerical_serial_fea if fea not in ['Init Fwd Win Byts', 'Init Bwd Win Byts']]\n", 1045 | "selector = SelectKBest(chi2, k=5)\n", 1046 | "selector = selector.fit(data[chi2_test_fea], data['Label'])\n", 1047 | "features_mask = selector.get_support(indices=True)\n", 1048 | "selected_features = np.array(chi2_test_fea)[features_mask]\n", 1049 | "print('Selected:', selected_features)" 1050 | ] 1051 | }, 1052 | { 1053 | "cell_type": "code", 1054 | "execution_count": 50, 1055 | "metadata": {}, 1056 | "outputs": [ 1057 | { 1058 | "name": "stdout", 1059 | "output_type": "stream", 1060 | "text": [ 1061 | "Selected: ['Flow IAT Max' 'Fwd Header Len' 'Pkt Len Max' 'Init Fwd Win Byts'\n", 1062 | " 'Fwd Seg Size Min']\n" 1063 | ] 1064 | } 1065 | ], 1066 | "source": [ 1067 | "selector = SelectKBest(mutual_info_classif, k=5)\n", 1068 | "selector = selector.fit(data[numerical_serial_fea], data['Label'])\n", 1069 | "features_mask = selector.get_support(indices=True)\n", 1070 | "selected_features = np.array(numerical_serial_fea)[features_mask]\n", 1071 | "print('Selected:', selected_features)" 1072 | ] 1073 | }, 1074 | { 1075 | "cell_type": "code", 1076 | "execution_count": 56, 1077 | "metadata": {}, 1078 | "outputs": [ 1079 | { 1080 | "name": "stdout", 1081 | "output_type": "stream", 1082 | "text": [ 1083 | "Selected: ['Dst Port' 'Fwd Pkts/s' 'Down/Up Ratio' 'Init Fwd Win Byts'\n", 1084 | " 'Fwd Seg Size Min']\n" 1085 | ] 1086 | } 1087 | ], 1088 | "source": [ 1089 | "features = [fea for fea in data.columns if fea not in ['Timestamp', 'Label']]\n", 1090 | "selector = RFE(DecisionTreeClassifier(), n_features_to_select=5, step=1)\n", 1091 | "selector = selector.fit(data[features], data['Label'])\n", 1092 | "features_mask = selector.get_support(indices=True)\n", 1093 | "selected_features = np.array(features)[features_mask]\n", 1094 | "print('Selected:', selected_features)" 1095 | ] 1096 | }, 1097 | { 1098 | "cell_type": "code", 1099 | "execution_count": 52, 1100 | "metadata": {}, 1101 | "outputs": [ 1102 | { 1103 | "name": "stdout", 1104 | "output_type": "stream", 1105 | "text": [ 1106 | "Selected: ['Dst Port' 'Flow Duration' 'Flow Byts/s' 'Flow Pkts/s' 'Flow IAT Max'\n", 1107 | " 'Fwd IAT Tot' 'Fwd IAT Mean' 'Fwd IAT Std' 'Fwd IAT Max' 'Fwd IAT Min'\n", 1108 | " 'Bwd IAT Tot' 'Bwd IAT Mean' 'Bwd IAT Std' 'Bwd IAT Max' 'Bwd IAT Min'\n", 1109 | " 'Fwd Pkts/s' 'Pkt Len Var' 'Idle Mean' 'Idle Std' 'Idle Max' 'Idle Min']\n" 1110 | ] 1111 | } 1112 | ], 1113 | "source": [ 1114 | "selector = SelectFromModel(LogisticRegression(penalty='l2', C=10))\n", 1115 | "selector = selector.fit(data[features], data['Label'])\n", 1116 | "features_mask = selector.get_support(indices=True)\n", 1117 | "selected_features = np.array(features)[features_mask]\n", 1118 | "print('Selected:', selected_features)" 1119 | ] 1120 | }, 1121 | { 1122 | "cell_type": "code", 1123 | "execution_count": 55, 1124 | "metadata": {}, 1125 | "outputs": [ 1126 | { 1127 | "name": "stdout", 1128 | "output_type": "stream", 1129 | "text": [ 1130 | "Selected: ['Dst Port' 'Fwd Seg Size Min']\n" 1131 | ] 1132 | } 1133 | ], 1134 | "source": [ 1135 | "selector = SelectFromModel(DecisionTreeClassifier())\n", 1136 | "selector = selector.fit(data[features], data['Label'])\n", 1137 | "features_mask = selector.get_support(indices=True)\n", 1138 | "selected_features = np.array(features)[features_mask]\n", 1139 | "print('Selected:', selected_features)" 1140 | ] 1141 | }, 1142 | { 1143 | "cell_type": "code", 1144 | "execution_count": null, 1145 | "metadata": {}, 1146 | "outputs": [], 1147 | "source": [] 1148 | } 1149 | ], 1150 | "metadata": { 1151 | "kernelspec": { 1152 | "display_name": "Python 3", 1153 | "language": "python", 1154 | "name": "python3" 1155 | }, 1156 | "language_info": { 1157 | "codemirror_mode": { 1158 | "name": "ipython", 1159 | "version": 3 1160 | }, 1161 | "file_extension": ".py", 1162 | "mimetype": "text/x-python", 1163 | "name": "python", 1164 | "nbconvert_exporter": "python", 1165 | "pygments_lexer": "ipython3", 1166 | "version": "3.7.9" 1167 | } 1168 | }, 1169 | "nbformat": 4, 1170 | "nbformat_minor": 4 1171 | } 1172 | -------------------------------------------------------------------------------- /draw_pics.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import matplotlib.pyplot as plt" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 2, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "lgb_accuracy_score_list = [0.999980926495482, 0.9999856948716115, 0.9999666213670935, 0.999990463247741, 0.9999570846148345]\n", 19 | "lgb_f1_score_list = [0.9998095419483859, 0.9998571496595401, 0.9996667142789125, 0.9999047619047619, 0.9995716121662145]\n", 20 | "lgb_auc_score_list = [0.99999999856567, 0.9999999985656699, 0.9999999684475853, 0.9999999885263947, 0.9999999792040903]\n", 21 | "\n", 22 | "xgb_accuracy_score_list = [0.999961852990964, 0.999980926495482, 0.9999475478625754, 0.9999380111103163, 0.9999427794864459]\n", 23 | "xgb_f1_score_list = [0.9996189750428652, 0.9998095056672064, 0.999476165531692, 0.99938074596294, 0.9994285714285714]\n", 24 | "xgb_auc_score_list = [0.9999995013312458, 0.9999994998969156, 0.999999535318984, 0.9999995109375728, 0.9999994994639676]" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 3, 30 | "metadata": {}, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "image/png": 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\n", 35 | "text/plain": [ 36 | "
" 37 | ] 38 | }, 39 | "metadata": { 40 | "needs_background": "light" 41 | }, 42 | "output_type": "display_data" 43 | } 44 | ], 45 | "source": [ 46 | "plt.figure(figsize=(18, 4))\n", 47 | "plt.subplot(131)\n", 48 | "plt.plot([str(i) for i in range(5)], lgb_accuracy_score_list, label='LightGBM', marker='o')\n", 49 | "plt.plot([str(i) for i in range(5)], xgb_accuracy_score_list, label='XGBoost', marker='s')\n", 50 | "plt.ylim(0.9999, 1.0)\n", 51 | "plt.xlabel('$K^{th}$ Fold')\n", 52 | "plt.ylabel('Accuracy')\n", 53 | "plt.grid()\n", 54 | "plt.legend()\n", 55 | "plt.subplot(132)\n", 56 | "plt.plot([str(i) for i in range(5)], lgb_f1_score_list, label='LightGBM', marker='o')\n", 57 | "plt.plot([str(i) for i in range(5)], xgb_f1_score_list, label='XGBoost', marker='s')\n", 58 | "plt.ylim(0.999, 1.0)\n", 59 | "plt.xlabel('$K^{th}$ Fold')\n", 60 | "plt.ylabel('F1 Score')\n", 61 | "plt.grid()\n", 62 | "plt.legend()\n", 63 | "plt.subplot(133)\n", 64 | "plt.plot([str(i) for i in range(5)], lgb_auc_score_list, label='LightGBM', marker='o')\n", 65 | "plt.plot([str(i) for i in range(5)], xgb_auc_score_list, label='XGBoost', marker='s')\n", 66 | "plt.ylim(0.999999, 1.0)\n", 67 | "plt.xlabel('$K^{th}$ Fold')\n", 68 | "plt.ylabel('AUC')\n", 69 | "plt.grid()\n", 70 | "plt.legend()\n", 71 | "plt.savefig('./pics/acc_f1_auc_lgb_xgb.png', bbox_inches='tight')" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 69, 77 | "metadata": {}, 78 | "outputs": [], 79 | "source": [ 80 | "top_ten_important_features = [('Fwd_Seg_Size_Min', 9338911.301903933), ('Dst_Port', 1057550.6179607362), ('Fwd_Header_Len', 410228.37797785224), ('Init_Fwd_Win_Byts', 89286.21361017204), ('Flow_Byts/s', 77586.27008461952), ('Flow_IAT_Max', 55537.81665795727), ('Pkt_Len_Max', 45023.481026887894), ('Fwd_IAT_Min', 36902.22046112176), ('Fwd_Pkts/s', 17839.881527069956), ('Flow_Pkts/s', 17816.451269016834)]" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 70, 86 | "metadata": {}, 87 | "outputs": [], 88 | "source": [ 89 | "x = [fea[0] for fea in top_ten_important_features]\n", 90 | "y = [fea[1] for fea in top_ten_important_features]\n", 91 | "x.reverse()\n", 92 | "y.reverse()" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": 72, 98 | "metadata": {}, 99 | "outputs": [ 100 | { 101 | "data": { 102 | "image/png": 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\n", 103 | "text/plain": [ 104 | "
" 105 | ] 106 | }, 107 | "metadata": { 108 | "needs_background": "light" 109 | }, 110 | "output_type": "display_data" 111 | } 112 | ], 113 | "source": [ 114 | "plt.figure(figsize=(8, 6))\n", 115 | "plt.barh(x, y)\n", 116 | "plt.xlabel('Importance')\n", 117 | "plt.ylabel('Features')\n", 118 | "plt.grid()\n", 119 | "plt.savefig('./pics/feature_importance.png', bbox_inches='tight')" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": null, 125 | "metadata": {}, 126 | "outputs": [], 127 | "source": [] 128 | } 129 | ], 130 | "metadata": { 131 | "kernelspec": { 132 | "display_name": "Python 3", 133 | "language": "python", 134 | "name": "python3" 135 | }, 136 | "language_info": { 137 | "codemirror_mode": { 138 | "name": "ipython", 139 | "version": 3 140 | }, 141 | "file_extension": ".py", 142 | "mimetype": "text/x-python", 143 | "name": "python", 144 | "nbconvert_exporter": "python", 145 | "pygments_lexer": "ipython3", 146 | "version": "3.7.9" 147 | } 148 | }, 149 | "nbformat": 4, 150 | "nbformat_minor": 4 151 | } 152 | --------------------------------------------------------------------------------