• DocumentCode
    2396728
  • Title

    Refinement of rule-based intrusion detection system for denial of service attacks by support vector machine

  • Author

    Chan, Aki P F ; Ng, Wing W Y ; Yeung, Daniel S. ; Tsang, Eric C C

  • Author_Institution
    Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China
  • Volume
    7
  • fYear
    2004
  • fDate
    26-29 Aug. 2004
  • Firstpage
    4252
  • Abstract
    With the tremendous increase in connectivity and accessibility to the Internet, information security has become a serious global issue. Denial of service (DoS), one of the attacks evolved in recent years, has devastating effect to the commercial activities. We propose a hybrid intrusion detection system (HIDS) which incorporates the benefits of both rule-based and SVM techniques. In brief, the SVM is used to select important features and generate rules, while the rule-based system is then applied to detect the DoS attacks. The rule set generated by the HIDS is more accurate and compact. Experimental results show that the HIDS has a better performance than the rule-based system with rules extracted only from human experts.
  • Keywords
    Internet; feature extraction; knowledge acquisition; knowledge based systems; learning (artificial intelligence); security of data; support vector machines; Internet; SVM; denial of service attack detection; feature selection; human experts; hybrid intrusion detection system; information security; learning mechanism; rule based system; rule generation; rules extraction; support vector machine; Business; Companies; Computer crime; Computer security; Humans; Information security; Internet; Intrusion detection; Protection; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
  • Print_ISBN
    0-7803-8403-2
  • Type

    conf

  • DOI
    10.1109/ICMLC.2004.1384585
  • Filename
    1384585