• DocumentCode
    2202540
  • Title

    A method for HMM-based system calls intrusion detection based on hybrid training algorithm

  • Author

    Wang, Panhong ; Shi, Liang ; Wang, Beizhan ; Liu, Yangbin ; Wu, Yuanqin

  • Author_Institution
    Software Sch., Xiamen Univ., Xiamen, China
  • fYear
    2011
  • fDate
    6-8 June 2011
  • Firstpage
    339
  • Lastpage
    342
  • Abstract
    HMM (Hidden Markov Model) is a very important intrusion detection tool. The classical HMM training algorithm is a climbing algorithm. It can only find a local optimal solution. To improve the accuracy of HMM training, this paper introduces a hybrid algorithm into intrusion detection. Experiments show that this algorithm can find a more accurate model.
  • Keywords
    computer network security; hidden Markov models; HMM based system calls intrusion detection; climbing algorithm; hidden Markov model; hybrid training algorithm; Algorithm design and analysis; Computational modeling; Data models; Evolutionary computation; Hidden Markov models; Intrusion detection; Training; Anomaly intrusion detection; Evolutionary computation; HMM; System call;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Automation (ICIA), 2011 IEEE International Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-1-4577-0268-6
  • Electronic_ISBN
    978-1-4577-0269-3
  • Type

    conf

  • DOI
    10.1109/ICINFA.2011.5949013
  • Filename
    5949013
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2202540