Empirical study on fusion methods using ensemble of RBFNN for network intrusion detection

被引：0

作者：

Chan, Aki Pr ^{[1
]}

Yeung, Daniel S. ^{[1
]}

Tsang, Eric C. C. ^{[1
]}

Ng, Wing W. Y. ^{[1
]}

机构：

[1] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China

来源：

ADVANCES IN MACHINE LEARNING AND CYBERNETICS | 2006年 / 3930卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The network security problem has become a critical issue and many approaches have been proposed to tackle the information security problems, especially the Denial of Service (DoS) attacks. Multiple Classifier System (MCS) is one of the approaches that have been adopted in the detection of DoS attacks recently. Fusion strategy is crucial and has great impact on the classification performance of an MCS. However the selection of the fusion strategy for an MCS in DoS problem varies widely. In this paper, we focus on the comparative study on adopting different fusion strategies for an MCS in DoS problem.

引用

页码：682 / 690

页数：9

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