计算机技术与发展Issue(2)：148-151,4.DOI:10.3969/j.issn.1673-629X.2015.02.034
基于GA和组合核的SVM入侵检测算法
Intrusion Detection Algorithm of SVM Based on GA and Composed Kernel Function
摘要
Abstract
SVM has a strong learning ability,has become one of the most important intrusion detection algorithm. Due to a large amount of raw data in intrusion detection,and with a high dimension,redundancy,etc. ,result in larger of calculating the volume and the longer of predicted time in the traditional SVM intrusion detection algorithm. Based on this,propose an improved SVM intrusion detection algo-rithm ( KPCA-GA-LC-SVM) . In this paper,use Kernel Principal Component Analysis ( KPCA) for data feature extraction and reduce the dimensionality of data and computation. Use a combination of kernel functions formed by weighted linear combination of two kernel function instead of the traditional single kernel function,and through genetic algorithm to find the optimization of kernel parameters and the weights of the composed kernel function to improve the performance of SVM. The experimental results show that the improved algo-rithm can effectively improve the accuracy of intrusion detection.关键词
入侵检测/核主成分分析法/支持向量机/遗传算法Key words
IDS/KPCA/SVM/GA分类
信息技术与安全科学引用本文复制引用
陈桂林,王生光,徐静妹,李雷..基于GA和组合核的SVM入侵检测算法[J].计算机技术与发展,2015,(2):148-151,4.基金项目
国家自然科学基金资助项目(61070234,61071167,61373137) （61070234,61071167,61373137）
国家大学生创新创业训练计划项目(SZDG2013032) （SZDG2013032）
