首页|基于生成对抗网络和混合时空神经网络的入侵检测

基于生成对抗网络和混合时空神经网络的入侵检测

Intrusion detection based on generative adversarial networks and hybrid spatio-temporal neural networks

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  • 维普
  • 万方数据
针对网络入侵检测领域存在检测准确率低的问题,研究异常流量样本少和分类器性能不佳时的入侵检测模型,提出一种基于改进生成对抗网络和混合时空神经网络的入侵检测模型.改进生成对抗网络通过学习异常流量样本的分布特性,生成具有特定标签的人工异常流量样本;融合卷积神经网络和双向长短时记忆神经网络提取攻击流量的时空融合特征,利用注意力机制对时空融合特征进行加权,构建混合时空神经网络对网络流量进行分类预测.在UNSW-NB15数据集上对所提模型进行仿真实验,准确率和F1分数分别为92.93%和94.81%,表明所提模型能够有效改善原始数据集中的类别不平衡性问题,提高对异常流量样本的检测能力和网络入侵的检测准确率.
Aiming at the problem of low detection accuracy in the field of network intrusion detection,we study the intrusion detection model when there are few samples of anomalous traffic and the performance of classifiers is poor,and propose an intrusion detection model based on improved generative adversarial network and hybrid spatio-temporal neural network.The improved generative adversarial network generates artificial anomalous traffic samples with specific labels by learning the distribution characteristics of the anomalous traffic samples;the fusion convolutional neural network and bidirectional long and short-term memory neural network extracts the spatio-temporal fusion features of the attacking traffic,and utilizes the attention mechanism to weight the spatio-temporal fusion features and constructs a hybrid spatio-temporal neural network to classify and predict the network traffic.Simulation experiments of the proposed model are conducted on the UNSW-NB15 dataset,and the accuracy and F1 score are 92.93%and 94.81%,respectively,indicating that the proposed model can effectively improve the problem of category imbalance in the original dataset,and improve the detection capability of abnormal traffic samples and the detection accuracy of network intrusion.

network intrusion detectiongenerative adversarial networksconvolutional neural networksbi-directional long and short-term memory neural networksattention mechanisms

倪志伟、行鸿彦、侯天浩、梁欣怡、王心怡

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南京信息工程大学电子与信息工程学院 南京 210044

网络入侵检测 生成对抗网络 卷积神经网络 双向长短时记忆神经网络 注意力机制

国家自然科学基金国家重点研发计划

621712282021YFE0105500

2024

10.19651/j.cnki.emt.2314597

电子测量技术
北京无线电技术研究所

电子测量技术

CSTPCD北大核心
影响因子：1.166
ISSN：1002-7300
年,卷(期)：2024.47(2)
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