基于混合CatfishPSO-LSSVM特征选择的入侵检测  

Intrusion Detection Based on Hybrid CatfishPSO-LSSVM Feature Selection

作　　者：王卫平[1] 唐志煦[1] 

机构地区：[1]中国科学技术大学管理学院,合肥230026

出　　处：《计算机系统应用》2012年第1期85-89,共5页Computer Systems & Applications

摘　　要：入侵检测系统面临的主要问题是计算量大,特征选择被引入解决这一问题。针对现有方法的缺点,利用改进的粒子群算法来搜索最优特征子集,提出了一种基于混合CatfishPSO和最小二乘支持向量机的特征选择方法,利用混合的CatfishBPSO和CatfishPSO选择特征子集并同步对LSSVM的参数进行优化,最后建立了一个基于该特征选择方法的入侵检测模型。在KDD Cup 99数据集上进行的实验结果表明该模型的检测性能较高。The main issue of Intrusion detection systems is large computation,feature selection was introduced to solve the problem.According to the shortcomings of existing methods,this paper uses improved Particle Swarm Optimization to search optimal feature subset,proposes a feature selection method based on hybrid CatfishPSO and Least Square Support Vector Machine,uses combined CatfishBPSO and CatfishPSO to select feature subset and optimize the parameters of LSSVM simultaneously,and build a Intrusion detection model based on the feature selection method above.Experiments on KDD Cup 99 show that the model has a good detection performance.

关 键 词：特征选择 粒子群算法 最小二乘支持向量机 入侵检测 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]