Study of Neural Network Technologies in Intrusion Detection Systems

被引：0

作者：

Fu Yanwei ^{[1
]}

Zhu Yingying ^{[2
]}

Yu Haiyang ^{[2
]}

机构：

[1] Jiangsu Polytech Univ, Network Ctr, Changzhou, Peoples R China

[2] Jiangsu Polytech Univ, Inst Sci & Informat Engn, Changzhou, Peoples R China

来源：

2009 5TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-8 | 2009年

关键词：

Neural Network; Intrusion Detection; Adaptive Resonance Theory; Genetic Algorithm;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In recent years, the network attack become more and more widespread and difficult in against. Intrusion Detection is a major focus of research in network security. This paper analyzes Neural Network (NN) methods being used in IDS, in which five different types of NNs are described: multilayer perceptrons (MLP), radial basis function (RBF), self-organizing feature map (SOFM), adaptive resonance theory (ART) and principal component analysis (PCA). An intrusion detection system combined with Genetic Algorithm (GA) and Back Propagation (BP) network is presented. Finally, a discussion of the future NN technologies, which guarantee to enhance the detection efficiency of IDS is provided.

引用

页码：4454 / +

页数：2

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