• DocumentCode
    2497974
  • Title

    Intrusion detection based on hidden Markov model

  • Author

    Yin, Qing-Bo ; Shen, Li-ran ; Zhang, Ru-bo ; Li, Xue-Yao ; Wang, Hui-Qiang

  • Author_Institution
    Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., China
  • Volume
    5
  • fYear
    2003
  • fDate
    2-5 Nov. 2003
  • Firstpage
    3115
  • Abstract
    The intrusion detection technologies of the network security are researched, and the technologies of pattern recognition are used to intrusion detection. Intrusion detection rely on a wide variety of observable data to distinguish between legitimate and illegitimate activities. Hidden Markov Model (HMM) has been successfully used in speech recognition and some classification areas. Since Anomaly Intrusion Detection can be treated as a classification problem, some basic ideas have been proposed on using HMM to model normal behavior. The experiments have showed that the method based on HMM is effective to detect anomalistic behaviors.
  • Keywords
    feature extraction; hidden Markov models; pattern recognition; security of data; HMM; anomalistic behavior detection; classification; hidden Markov Model; illegitimate activities; intrusion detection; legitimate activities; network security; observable data; pattern recognition; speech recognition; Computer science; Computer security; Data engineering; Data security; Educational institutions; Handwriting recognition; Hidden Markov models; Intrusion detection; Pattern recognition; Speech recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2003 International Conference on
  • Print_ISBN
    0-7803-8131-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2003.1260114
  • Filename
    1260114