• DocumentCode
    2767265
  • Title

    Research on SVM Based Network Intrusion Detection Classification

  • Author

    Xie, Lixia ; Zhu, Dan ; Yang, Hongyu

  • Author_Institution
    Sch. of Comput. Sci., Civil Aviation Univ. of China, Tianjin, China
  • Volume
    7
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    362
  • Lastpage
    366
  • Abstract
    This paper presents a new network intrusion detection classification model based on the Support vector machine (SVM). In this model, the factor analysis (FA) algorithm converted a large number of related network behaviors features into concise integrated features, and the support vector decision function ranking method (SVDFRM) calculated the contribution of network behaviors features. Then some important network behaviors features were extracted and network behaviors were classified consequently. The experimental results show that the detection rate and the real-time of this classification model are satisfying.
  • Keywords
    real-time systems; security of data; support vector machines; SVM research; concise integrated features; factor analysis algorithm; network behaviors features; network intrusion detection classification; real time classification model; support vector decision function ranking method; Algorithm design and analysis; Classification algorithms; Computer science; Covariance matrix; Feature extraction; Fuzzy systems; Intrusion detection; Pattern analysis; Support vector machine classification; Support vector machines; SVM; classificaiton; feature; network intrusion detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3735-1
  • Type

    conf

  • DOI
    10.1109/FSKD.2009.45
  • Filename
    5360018