计算机工程与应用Issue(2)：99-102,4.DOI:10.3778/j.issn.1002-8331.1204-0438
基于粗糙集与改进LSSVM的入侵检测算法研究
Study on intrusion detection algorithm based on rough set theory and improved LSSVM
刘其琛 1施荣华 2王国才 2穆炜炜1
作者信息
- 1. 湖南化工职业技术学院，湖南 株洲 412004
- 2. 中南大学 信息科学与工程学院，长沙 410083
- 折叠
摘要
Abstract
This thesis proposes the intrusion detection algorithm based on rough set and the improved least squares support vector machine. The algorithm reduces sample attributes by discernible matrix using rough set theory, reduces the dimen-sion of the data samples. It improves the least squares support vector machine by a sparse algorithm, so it can improve the veracity of data sample classification with the sparse characteristic and rapid detection. On the one hand the combined algorithm has the advantages that rough set can reduce the data effectively and the support vector machine can classify accurately, and on the other hand it avoids the poor generalization while the rough set is in the noise environment and overcomes the limitations when support vector machine identifies effective data and redundant data. Experimental results show that intrusion detection algorithm based on rough set and the improved least squares support vector machine has high detection accuracy, low false positive rate and false negative rate and short detection time which show the validity of the algorithm.关键词
入侵检测/粗糙集理论/支持向量机Key words
intrusion detection/rough sets theory/support vector machine分类
信息技术与安全科学引用本文复制引用
刘其琛,施荣华,王国才,穆炜炜..基于粗糙集与改进LSSVM的入侵检测算法研究[J].计算机工程与应用,2014,(2):99-102,4.
