Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (11): 123-125.DOI: 10.3778/j.issn.1002-8331.2009.11.038

• 网络、通信、安全 • Previous Articles     Next Articles

Application of combined kernel SVM on network security risk evaluation

GAO Hui-sheng,GUO Ai-ling   

  1. Department of Electronic and Telecommunication Engineering,North China Electric Power University,Baoding,Hebei 071003,China
  • Received:2008-02-25 Revised:2008-05-12 Online:2009-04-11 Published:2009-04-11
  • Contact: GAO Hui-sheng

组合核函数SVM在网络安全风险评估中的应用

高会生,郭爱玲   

  1. 华北电力大学,电子与通信工程系,河北 保定 071003
  • 通讯作者: 高会生

Abstract: Kernel function is the key technology of SVM,the choice of kernel will affect the learning ability and generalization ability of SVM.Since every traditional kernel has its advantages and disadvantages,this paper analyzes the principle of traditional kernels and adopts a new kernel of combined kernel which has better generalization ability and better learning ability,and adopts the combined kernel SVM into network security risk evaluation,and then compares with the SVM using traditional kemels.The results show that the SVM based on combined kemels has batter speed and higher force of classification than that with traditional kernels.

Key words: Support Vector Machine(SVM), combined kernel, network security, risk evaluation

摘要: 核函数是SVM的关键技术,核函数的选择将影响着支持向量机的学习能力和泛化能力。各个普通核函数各有利弊,在分析各个普通核函数的基础上,采用了一种新的组合核函数,它既具有很好的泛化能力,也具有很好的学习能力,并将其构造的支持向量机应用到网络安全的风险评估中,与普通核函数构造的支持向量机的评估效果进行比较。结果表明组合核函数支持向量机不仅提高了分类速度,而且具有较高的分类精度。

关键词: 支持向量机, 组合核函数, 网络安全, 风险评估