• DocumentCode
    2558360
  • Title

    An Intelligent Intrusion Detection and Response System Using Hybrid Ward Hierarchical Clustering Analysis

  • Author

    Hooper, Emmanuel

  • Author_Institution
    Univ. of London Royal Holloway, Egham
  • fYear
    2007
  • fDate
    26-28 April 2007
  • Firstpage
    1187
  • Lastpage
    1192
  • Abstract
    Intelligent intrusion and detection strategies for reducing false positives and increasing detection within real network infrastructures has been a major challenge in information security. Current strategies often lack real network infrastructure detection and responses for distinguishing between benign traffic and complex attacks. This intelligent hybrid detection and response strategies distinguishes between real attack and normal traffic. This novel strategy consists of a hybrid statistical analysis involving Ward´s hierarchical clustering. This results of the hybrid statistical analysis is fed back to the IDS´ alert monitor to identify real attacks and isolate benign traffic. This intelligent detection and response strategy enhances the ability of the IDS to accurately detect and respond to subsequent threats and benign traffic in critical segments of real network infrastructures.
  • Keywords
    knowledge based systems; security of data; statistical analysis; benign traffic; hybrid statistical analysis; hybrid ward hierarchical clustering analysis; information security; infrastructure detection; intelligent intrusion detection; normal traffic; response system; Clustering methods; Data mining; Information analysis; Information security; Intelligent networks; Intrusion detection; Learning systems; Performance analysis; Statistical analysis; Telecommunication traffic;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Ubiquitous Engineering, 2007. MUE '07. International Conference on
  • Conference_Location
    Seoul
  • Print_ISBN
    0-7695-2777-9
  • Type

    conf

  • DOI
    10.1109/MUE.2007.80
  • Filename
    4197440