• DocumentCode
    505014
  • Title

    Intrusion detection system combining misuse detection and anomaly detection using Genetic Network Programming

  • Author

    Gong, Yunlu ; Mabu, Shingo ; Chen, Ci ; Wang, Yifei ; Hirasawa, Kotaro

  • Author_Institution
    Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitakyushu, Japan
  • fYear
    2009
  • fDate
    18-21 Aug. 2009
  • Firstpage
    3463
  • Lastpage
    3467
  • Abstract
    In this paper, a class association rule mining approach based on Genetic Network Programming (GNP) for detecting network intrusion combining misuse detection and anomaly detection is proposed. The proposed approach is an extension of the intrusion detection approach using GNP, so it can detect and distinguish normal, known intrusion and unknown intrusion. The simulation result shows that the detection rate is improved compared with traditional intrusion detection approach, and normal, known intrusion and unknown intrusion are distinguished with high accuracy.
  • Keywords
    genetic algorithms; security of data; anomaly detection; class association rule mining; genetic network programming; known intrusion; misuse detection; network intrusion detection system; normal intrusion; unknown intrusion; Association rules; Computer networks; Data mining; Databases; Economic indicators; Genetics; Intrusion detection; Mathematical programming; Production systems; Protection; Genetic Network Programming; class association rule mining; network intrusion detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    ICCAS-SICE, 2009
  • Conference_Location
    Fukuoka
  • Print_ISBN
    978-4-907764-34-0
  • Electronic_ISBN
    978-4-907764-33-3
  • Type

    conf

  • Filename
    5335129