北京交通大学学报2013，Vol.37Issue(5)：58-61,4.
基于特征选择和SVM参数同步优化的网络入侵检测
Network intrusion detection based on simultaneous optimization of features selection and parameters of support vector machine
摘要
Abstract
In order to improve network intrusion detection rate,this paper proposed a network intrusion detection algorithm based on simultaneous optimization of feature selection and SVM parameters which used the relationship between the feature selection and SVM parameters.Firstly,the network intrusion detection rate as the objection function to built mathematical model which the constraint conditions were the feature and SVM parameters.Secondly,the genetic algorithm was used to get the optimal features and SVM parameters.Lastly,the performance of the proposed algorithm was tested by KDD 1999 data.The results showed that the proposed algorithm could select the optimal features and SVM parameters to improve the network intrusion detection rate and detection speed compared with other network intrusion detection algorithms.关键词
支持向量机/遗传算法/网络入侵检测/特征选择Key words
support vector machine (SVM) / genetic algorithm/ network intrusion detection/ feature selection分类
信息技术与安全科学引用本文复制引用
樊爱宛,时合生..基于特征选择和SVM参数同步优化的网络入侵检测[J].北京交通大学学报,2013,37(5):58-61,4.基金项目
河南省科技计划重点项目资助(102102210416) （102102210416）
