Intrusion detection using multiple classifiers fusion and clustering analysis

被引：0

作者：

Zhong, Cheng ^{[1
]}

Mi, Aizhong ^{[1
]}

Yang, Feng ^{[1
]}

机构：

[1] Guangxi Univ, Sch Comp & Elect & Informat, Nanning 530004, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE INFORMATION COMPUTING AND AUTOMATION, VOLS 1-3 | 2008年

关键词：

clustering analysis; decision fusion; intrusion detection; multiple classifiers fusion;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Decision fusion of multiple classifiers can obtain more accurately classification than the best single classifier. By applying multiple classifiers fusion and clustering analysis and the nearest neighbor rule respectively, this paper presents a new intrusion detection algorithm. The analysis and experiment results show that this algorithm can achieve a good detection performance and reduce remarkably the errors and false alarms for intrusion detection.

引用

下载

页码：1181 / 1183

页数：3

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