[1]陈桂林,王生光,徐静妹,等. 基于GA和组合核的SVM入侵检测算法[J].计算机技术与发展,2015,25(02):148-151.
　CHEN Gui-lin,WANG Sheng-guang,XU Jing-mei,et al. Intrusion Detection Algorithm of SVM Based on GA and Composed Kernel Function[J].,2015,25(02):148-151.
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 基于GA和组合核的SVM入侵检测算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年02期
页码:
148-151
栏目:
智能、算法、系统工程
出版日期:
2015-02-10

文章信息/Info

Title:
 Intrusion Detection Algorithm of SVM Based on GA and Composed Kernel Function
文章编号:
1673-629X（2015)02-0148-04
作者:
 陈桂林; 王生光; 徐静妹; 李雷
 南京邮电大学
Author(s):
 CHEN Gui-lin; WANG Sheng-guang; XU Jing-mei; LI Lei
关键词:
 ; 入侵检测; 核主成分分析法; 支持向量机; 遗传算法
Keywords:
 ; IDS; KPCA; SVM; GA
分类号:
TP301.6
文献标志码:
A
摘要:
 SVM有着很强的学习能力，已经成为入侵检测的重要算法之一。由于入侵检测原始数据量大，且具有高维性、冗余性等特点，导致传统SVM入侵检测算法计算量大、预测时间长。基于此，文中提出一种改进的SVM入侵检测算法( KP-CA-GA-LC-SVM)。文中利用核主成分分析法( KPCA)进行数据的特征提取，降低数据维数和计算量；使用两个核函数线性加权结合形成的组合核函数代替传统的单一核函数，并通过遗传算法( GA)进行SVM核参数及组合核权系数的寻优，来提高SVM性能。实验结果表明，文中算法有效地提高了入侵检测的检测精度。
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.

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更新日期/Last Update: 2015-04-28