计算机工程与应用Issue(20)：87-90,4.DOI:10.3778/j.issn.1002-8331.1304-0298
特征选择和分类器优化耦合的网络入侵检测
NIU Lei, et al. Network intrusion detection based on considering features selection and classifier optimization simultaneously
摘要
Abstract
In order to solve mismatch problem of the feature selection and classifier parameters in network intrusion, this paper proposes a network intrusion detection model(F-SVM)based on coupling feature selection with classifier optimization. The evaluated standard of features is mapped into high-dimensional space by radial basis kernel function to calculate, and the rela-tion between the network feature evaluation and network intrusion classifier is established, so the feature selection stage has solved the parameter design of the classifier, the network intrusion detection model is established and the performance is tested using KDD 99 data. The results show that F-SVM can eliminate unnecessary, redundant features, dimension of network charac-teristics is significantly reduced, and the optimal parameters of network intrusion classifier are obtained, which improves the net-work intrusion detection accuracy and detection efficiency.关键词
特征选择/分类器/网络入侵/参数优化/核函数参数Key words
features selection/classifier/network intrusion/parameter optimization/kernel function parameter分类
信息技术与安全科学引用本文复制引用
刘冬冬,王峰,牛磊,郭博..特征选择和分类器优化耦合的网络入侵检测[J].计算机工程与应用,2013,(20):87-90,4.基金项目
安徽省教育厅自然科学研究项目（No.KJ2013Z262，No.KJ2012Z313）；全国统计科学研究计划项目（No.2012LY009）。 （）
