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面向入侵检测的元图神经网络构建与分析

王振东 徐振宇 李大海 王俊岭

王振东, 徐振宇, 李大海, 王俊岭. 面向入侵检测的元图神经网络构建与分析. 自动化学报, 2021, 47(x): 1−19 doi: 10.16383/j.aas.c200819
引用本文: 王振东, 徐振宇, 李大海, 王俊岭. 面向入侵检测的元图神经网络构建与分析. 自动化学报, 2021, 47(x): 1−19 doi: 10.16383/j.aas.c200819
Wang Zhen-Dong, Xu Zhen-Yu, Li Da-Hai, Wang Jun-Ling. Construction and analysis of meta graph neural network for intrusion detection. Acta Automatica Sinica, 2021, 47(x): 1−19 doi: 10.16383/j.aas.c200819
Citation: Wang Zhen-Dong, Xu Zhen-Yu, Li Da-Hai, Wang Jun-Ling. Construction and analysis of meta graph neural network for intrusion detection. Acta Automatica Sinica, 2021, 47(x): 1−19 doi: 10.16383/j.aas.c200819

面向入侵检测的元图神经网络构建与分析

doi: 10.16383/j.aas.c200819
基金项目: 国家自然科学基金(62062037, 61763017), 江西省自然科学基金(20181BBE58018)资助
详细信息
    作者简介:

    王振东:博士, 江西理工大学信息工程学院副教授, 主要研究方向为无线传感器网络, 智慧物联网, 认知计算, 大数据与信息安全. E-mail: wangzhendong@hrbeu.edu.cn

    徐振宇:江西理工大学信息工程学院硕士研究生, 主要研究方向为信息安全, 本文通信作者. E-mail: xuzhenyu0208@163.com

    李大海:博士, 江西理工大学信息工程学院副教授. 主要研究方向为分布式系统服务质量(QoS)控制, 分布式系统自学习资源调度控制. E-mail: dlai6535@aliyun.com

    王俊岭:博士, 江西理工大学信息工程学院副教授. 主要研究方向为分布式计算, 容错, 计算机视觉. E-mail: wangjunling@jxust.edu.cn

Construction and Analysis of Meta Graph Neural Network for Intrusion Detection

Funds: Supported by National Science Foundation of China (62062037, 61763017), Natural Science Grant of Jiangxi Province (20181BBE58018)
More Information
    Author Bio:

    WANG Zhen-Dong Ph.D., Associate Professor at School of Information Engineering, Jiangxi University of Science and Technology, focuses on wireless sensor networks, smart Internet of Things, cognitive computing, big data and information security

    XU Zhen-Yu Master student of School of Information Engineering, Jiangxi University of Science and Technology, focuses on information security. Corresponding author of this paper

    LI Da-Hai Ph.D., associate professor at School of Information Engineering, Jiangxi University of Science and Technology, the main research direction is distributed system quality of service (QoS) control, distributed system self-learning resource scheduling control

    WANG Jun-Ling Ph.D., associate professor at School of Information Engineering, Jiangxi University of Science and Technology, the main research direction is distributed computing, fault tolerance, computer vision

  • 摘要: 网络入侵样本数据特征间存在未知的非欧式空间图结构关系, 深入挖掘并利用该关系可有效提升网络入侵检测方法的检测效能. 对此, 本文设计了一种元图神经网络(Meta graph neural network, MGNN), MGNN能够对样本数据特征内部隐藏的图结构关系进行挖掘与利用, 在应对入侵检测问题时优势明显. 首先, 设计了元图网络层MGNL, 挖掘出样本数据特征内部隐藏的图结构关系, 并利用该关系对样本数据的原始特征进行更新; 然后, 针对MGNN存在的图信息传播过程中父代信息湮灭现象提出反信息湮灭策略, 并设计了注意力损失函数, 简化MGNN中实现注意力机制的运算过程. KDD-NSL、UNSW-NB15、CICDoS2019数据集上的实验表明, 与经典深度学习算法DNN、CNN、RNN、LSTM和传统机器学习算法SVM、DT、RF、KNN、LR相比MGNN在准确率、F1值、精确率、召回率评价指标上均具有良好效果.
  • 图  1  MGNN结构与处理流程

    Fig.  1  Structure and processing flow of MGNN

    图  2  MGNN中单代间父子结点信息传递结构

    Fig.  2  Information transfer process of parent-child node between single generation in MGNN

    图  3  MGNN爷孙结点间信息传递结构

    Fig.  3  Information transfer process between grandparents and grandchildren in MGNN

    图  4  MGNN运行流程图

    Fig.  4  Operation flow chart of MGNN

    图  5  MGNNSB描述

    Fig.  5  Description of MGNNSB structure

    图  6  各神经网络对UNSW_NB15进行二分类

    Fig.  6  Each neural network performs a binary classification experiment on the UNSW_NB15

    图  7  各神经网络对NSL_KDD进行二分类

    Fig.  7  Each neural network performs a binary classification experiment on the NSL_KDD

    图  8  各神经网络对UNSW_NB15进行多分类

    Fig.  8  Each neural network performs multi-classification experiments on the UNSW_NB15

    图  9  各神经网络对NSL_KDD进行多分类

    Fig.  9  Each neural network performs multi-classification experiments on the NSL_KDD

    图  10  MGNN与最新入侵检测算法对比

    Fig.  10  Performance comparison between MGNN and the latest intrusion detection algorithms on different data sets

    图  11  各神经网络对UNSW_NB15进行二分类

    Fig.  11  Each neural network performs a binary classification experiment on the UNSW_NB15

    图  12  各神经网络对NSL_KDD进行二分类

    Fig.  12  Each neural network performs a binary classification experiment on the NSL_KDD

    图  13  各神经网络对UNSW_NB15进行多分类

    Fig.  13  Each neural network performs multi-classification experiments on the UNSW_NB15

    图  14  各神经网络对NSL_KDD进行多分类

    Fig.  14  Each neural network performs multi-classification experiments on the NSL_KDD

    图  15  注意力损失函数对MGNN影响

    Fig.  15  The effect of attention loss function on MGNN

    表  1  MGNN1~9网络各参数设置

    Table  1  Various parameter settings in the MGNN1~9 network

    网络类别MGNNSBNnPnunitsαActivation参数量
    MGNN11421641tanh287509
    26411281tanh
    312812681tanh
    426812681tanh
    MGNN31423641tanh287509
    26431281tanh
    312832681tanh
    426832681tanh
    MGNN51425641tanh287509
    26451281tanh
    312852681tanh
    426852681tanh
    MGNN71427641tanh287509
    26471281tanh
    312872681tanh
    426872681tanh
    MGNN91429641tanh287509
    26491281tanh
    312892681tanh
    426892681tanh
    下载: 导出CSV

    表  2  各算法对UNSW_NB15数据集二分类测试的结果

    Table  2  The experimental results of the binary classification test of each algorithm on the UNSW_NB15 data set

    算法AccuracyPrecisionRecallF1-score
    MGNN10.9020.9100.9120.911
    MGNN30.9290.9470.9240.935
    MGNN50.9400.9590.9310.945
    MGNN70.9430.9610.9330.947
    MGNN90.9450.9640.9350.949
    DNN0.8900.9010.8980.900
    CNN0.8530.8980.8270.861
    RNN0.7090.7220.7660.744
    LSTM0.8130.8770.7680.819
    RF0.9030.9880.8670.924
    LR0.7430.9550.6530.775
    KNN0.8100.9320.7780.848
    DT0.8970.9820.8640.919
    SVM_rbf0.6530.9980.4920.659
    下载: 导出CSV

    表  3  各算法对UNSW_NB15数据集多分类测试的结果

    Table  3  The experimental results of the multi-classification test of each algorithm on the UNSW_NB15 data set

    算法AccuracyPrecisionRecallF1-score
    MGNN10.7720.7350.7720.743
    MGNN30.8160.7870.8160.797
    MGNN50.8260.8010.8260.812
    MGNN70.8400.8240.8400.829
    MGNN90.8360.8150.8360.824
    DNN0.7620.7180.7620.724
    CNN0.6160.5300.6160.501
    RNN0.6400.4430.6400.521
    LSTM0.6600.5610.6600.566
    RF0.7550.7550.7550.724
    LR0.5380.4140.5380.397
    KNN0.6220.5780.6220.576
    DT0.7330.7210.7330.705
    SVM_rbf0.5810.5860.5810.496
    下载: 导出CSV

    表  4  各算法对NSL_KDD数据集二分类测试的结果

    Table  4  The experimental results of the binary classification test of each algorithm on the NSL_KDD data set

    算法AccuracyPrecisionRecallF1-score
    MGNN10.9850.9850.9820.984
    MGNN30.9860.9890.9810.985
    MGNN50.9860.9880.9810.985
    MGNN70.9900.9950.9850.990
    MGNN90.9720.9710.9700.970
    DNN0.9790.9750.9800.978
    CNN0.9790.9880.9670.977
    RNN0.9270.9250.9190.922
    LSTM0.9100.8950.9150.905
    RF0.9290.9460.9190.933
    LR0.8260.9150.7440.820
    KNN0.9100.9260.9050.915
    DT0.9300.9280.9430.935
    SVM_rbf0.8370.7690.9930.867
    下载: 导出CSV

    表  5  各算法对NSL_KDD数据集多分类测试的结果

    Table  5  The experimental results of the multi-classification test of each algorithm on the NSL_KDD data set

    算法AccuracyPrecisionRecallF1-score
    MGNN10.9860.9850.9860.985
    MGNN30.9870.9870.9870.987
    MGNN50.9860.9850.9860.985
    MGNN70.9750.9670.9750.971
    MGNN90.5330.2840.5330.371
    DNN0.9570.9550.9570.955
    CNN0.9700.9690.9700.968
    RNN0.8930.8840.8930.887
    LSTM0.8650.8660.8650.838
    RF0.7530.8140.7530.715
    LR0.6120.5090.6120.530
    KNN0.7310.7200.7310.684
    DT0.7630.7670.7630.728
    SVM_rbf0.7020.6890.7020.656
    下载: 导出CSV

    表  6  各算法对CICDoS2019数据集测试

    Table  6  Test results of each algorithm on the CICDoS2019 data set

    算法AccuracyPrecisionRecallF-score
    AttackBenignAttackBenignAttackBenign
    MGNN120.870.991.000.790.930.880.96
    NB0.571.000.530.171.000.290.69
    DT0.770.700.980.990.540.820.70
    LR0.950.930.990.990.910.960.95
    RF0.861.000.780.741.000.850.88
    Booster0.840.760.990.990.670.860.80
    SVM0.930.990.880.880.990.930.93
    DDoSNet0.990.991.000.990.990.990.99
    下载: 导出CSV

    表  7  MGNN9、MGNN9_alpha网络对UNSW_NB15数据集二分类测试的结果

    Table  7  MGNN9, MGNN9_alpha network on the UNSW_NB15 data set binary classification test results

    算法AccuracyPrecisionRecallF1-score
    MGNN90.9450.9640.9350.949
    MGNN9_alpha0.9510.9720.9390.955
    下载: 导出CSV

    表  8  MGNN9、MGNN9_alpha网络对UNSW_NB15数据集多分类测试的结果

    Table  8  MGNN9, MGNN9_alpha network on the UNSW_NB15 data set multi-classification test results

    算法AccuracyPrecisionRecallF1-score
    MGNN90.8360.8150.8360.824
    MGNN9_alpha0.8460.8310.8460.837
    下载: 导出CSV

    表  9  MGNN9、MGNN9_alpha网络对NSL_KDD数据集二分类测试的结果

    Table  9  MGNN9, MGNN9_alpha network on the NSL_KDD data set binary classification test results

    算法AccuracyPrecisionRecallF1-score
    MGNN90.9720.9710.9700.970
    MGNN9_alpha0.9920.9930.9900.991
    下载: 导出CSV

    表  10  MGNN9、MGNN9_alpha网络对NSL_KDD数据集多分类测试的结果

    Table  10  MGNN9, MGNN9_alpha network on the NSL_KDD data set multi-classification test results

    算法AccuracyPrecisionRecallF1-score
    MGNN90.5330.2840.5330.371
    MGNN9_alpha0.9870.9870.9870.986
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-10-01
  • 录用日期:  2021-01-19
  • 网络出版日期:  2021-03-27

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