• DocumentCode
    2192515
  • Title

    Intrusion Detection Model Based on Improved Support Vector Machine

  • Author

    Yuan, Jingbo ; Li, Haixiao ; Ding, Shunli ; Cao, Limin

  • Author_Institution
    Inst. of Inf. Manage. Technol. & Applic., Northeastern Univ. at Qinhuangdao, Qinhuangdao, China
  • fYear
    2010
  • fDate
    2-4 April 2010
  • Firstpage
    465
  • Lastpage
    469
  • Abstract
    With development and popularization of computer network, network security problems increasingly bring into prominence. Intrusion detection technique can effectively enlarge the scope of protection on network and system. An intrusion detection method based on support vector machine (SVM) is studied. Aiming at the shortcoming of SVM on detecting precision, an intrusion detection model based on improved SVM is put forward according to hypothesis test theory. To confirm the effectiveness of this approach, a simulation testing is done. The experiment results show that the improved SVM has stronger learning ability and higher accuracy and lower false positive rate.
  • Keywords
    security of data; support vector machines; SVM; hypothesis test theory; improved support vector machine; intrusion detection model; network security; Computer security; Data mining; Data security; Information security; Intrusion detection; Operating systems; Support vector machine classification; Support vector machines; Testing; Training data; hypothesis test theory; intrusion detection; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium on
  • Conference_Location
    Jinggangshan
  • Print_ISBN
    978-1-4244-6730-3
  • Electronic_ISBN
    978-1-4244-6743-3
  • Type

    conf

  • DOI
    10.1109/IITSI.2010.72
  • Filename
    5453618