Journal of South China University of Technology (Natural Science Edition) ›› 2011, Vol. 39 ›› Issue (2): 108-112,124.doi: 10.3969/j.issn.1000-565X.2011.02.018

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

A Reduced SVM-based Fast Intrusion Detection Model

Zhang Xue-qin1  Gu Chun-hua1  Wu Ji-yi2   

  1. 1. East China university of science and technology information science and engineering institute, Shanghai 200237; 2. Hangzhou normal university electronic business and information security key laboratory, zhejiang hangzhou 310036
  • Received:2010-04-28 Revised:2010-09-05 Online:2011-02-25 Published:2011-01-02
  • Contact: 张雪芹(1972-)，女，副教授，博士，主要从事信息安全研究 E-mail:zxq@ecust.edu.cn
  • About author:张雪芹(1972-)，女，副教授，博士，主要从事信息安全研究
  • Supported by:

    国家自然科学基金资助项目(60773094)；杭州市电子商务与信息安全重点实验室开放课题项目(HZEB201009)

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Abstract:

Owing to the constraints of time and space complexity,the standard SVM（Support Vector Machine） algorithm cannot effectively deal with large-scale network intrusion detection.In order to solve this problem and in view of the geometric interpretation of SVM,an intrusion detection classification algorithm named PCH-SVM is proposed based on the parallel convex hull decomposition and the SVM.With the help of convex hull decomposition and parallel computing,this algorithm can fast extract the vertices of convex hull of the original training samples to build a reduced SVM training set.Experimental results show that the proposed algorithm can effectively reduce the time and space complexity during SVM training,and speeds up the modeling and detection of intrusion detection classifier without any accuracy loss.

Key words: Intrusion Detection, Support Vector Machine, Sample Selection, Convex Hull