计算机应用与软件Issue(5)：305-307,333,4.DOI:10.3969/j.issn.1000-386x.2014.05.077
基于小波核主成分分析和差分进化优化极限学习机的入侵检测
INTRUSION DETECTION BASED ON WAVELET KERNEL PCA AND DE OPTIMISED EXTREME LEARNING MACHINE
摘要
Abstract
For network intrusion detection,we propose such a method which combines the wavelet kernel PCA and DE optimised extreme learning machine.First,the kernel principal component analysis (PCA)is applied to conduct the nonlinear dimensionality reduction on original data,in order to further improve nonlinear mapping ability of kernel PCA,wavelet kernel function is introduced as its kernel function. Then the extreme learning machine is used for the classification and recognition of the processed data,and the differential evolution (DE) algorithm is used to obtain the optimal initial weights for the unstable performance of the extreme learning machine caused by random selection of initial weights.Experimental results show that the algorithm proposed can effectively improve the recognition rate of intrusion detection and reduce the rates of false positives and false negatives.关键词
入侵检测/小波核主成分分析/极限学习机/差分进化Key words
Intrusion detection/Wavelet kernel principal component analysis/Extreme learning machine/Differential evolution分类
信息技术与安全科学引用本文复制引用
朱永胜,董燕,慕昆..基于小波核主成分分析和差分进化优化极限学习机的入侵检测[J].计算机应用与软件,2014,(5):305-307,333,4.基金项目
河南省重点科技攻关项目（122102210503）。 （）
