Intrusion Detection Classifier Based on Dynamic SOM and Swarm Intelligence Clustering

被引：1

作者：

Feng, Yong ^{[1
]}

Zhong, Jiang ^{[1
]}

Xiong, Zhong-yang ^{[1
]}

Ye, Chun-xiao ^{[1
]}

Wu, Kai-gui ^{[1
]}

机构：

[1] Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China

来源：

ADVANCES IN COGNITIVE NEURODYNAMICS, PROCEEDINGS | 2008年

关键词：

Intrusion detection; classifier; dsom; swarm intelligence; clustering;

D O I：

10.1007/978-1-4020-8387-7_167

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

A clustering analysis model based on dynamic self-organizing maps (DSOM) and swarm intelligence (SI) is systematically proposed for intrusion detection system. The basic idea of the model is to produce the cluster by DSOM and SI. With the classified data instances, the detection classifier can be established. And then the detection classifier can be used in real intrusion detection. Experimental results show that our detection classifier maintained a higher performance than SVM, LGP, DT and K-NN.

引用

页码：969 / +

页数：3

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