• DocumentCode
    2449234
  • Title

    Applied Research on Data Mining Algorithm in Network Intrusion Detection

  • Author

    Xue, Ming ; Zhu, Changjun

  • Author_Institution
    Changchun Inst. of Technol., Changchun, China
  • fYear
    2009
  • fDate
    25-26 April 2009
  • Firstpage
    275
  • Lastpage
    277
  • Abstract
    Intrusion detection is one of network security area of technology main research directions. Data mining technology was applied to network intrusion detection system (NIDS), may automatically discover the new pattern from the massive network data, to reduce the workload of the manual compilation intrusion behavior patterns and normal behavior patterns. This article reviewed the current intrusion detection technology and the data mining technology briefly. Focus on data mining algorithm in anomaly detection and misuse detection of specific applications. For misuse detection, the main study the classification algorithm; for anomaly detection, the main study the pattern comparison and the cluster algorithm. In pattern comparison to analysis deeply the association rules and sequence rules . Finally, has analysed the difficulties which the current data mining algorithm in intrusion detection applications faced at present, and has indicated the next research direction.
  • Keywords
    data mining; pattern classification; security of data; anomaly detection; association rules; classification algorithm; data mining algorithm; misuse detection; network intrusion detection system; network security; pattern discovery; sequence rules; Algorithm design and analysis; Association rules; Classification algorithms; Clustering algorithms; Data mining; Data security; Face detection; Intrusion detection; Manuals; Pattern analysis; classification; data mining; intrusion detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence, 2009. JCAI '09. International Joint Conference on
  • Conference_Location
    Hainan Island
  • Print_ISBN
    978-0-7695-3615-6
  • Type

    conf

  • DOI
    10.1109/JCAI.2009.25
  • Filename
    5158993