• DocumentCode
    686405
  • Title

    Incremental principal component analysis method on online network anomaly detection

  • Author

    Li Bai-nan ; Yao Dong ; Qian Ye-kui

  • Author_Institution
    Nat. Digital Switching Syst. Eng. & Technol. Res. Center, Zhengzhou, China
  • fYear
    2013
  • fDate
    22-24 Nov. 2013
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Although PCA (principal component analysis) based multivariate anomaly detection algorithm can perform detection task, it cannot satisfy the needs of online detection due to the time complexity. To conquer this limitation, a multivariate online anomaly detection algorithm based on incremental PCA (IPCA) was proposed. The algorithm constructed normal model of traffic matrix incrementally and implemented online detection with this model. Analysis with Internet real traffic data and simulation data shows that this algorithm can perform online anomaly detection effectively.
  • Keywords
    computational complexity; matrix algebra; principal component analysis; security of data; Internet real traffic data; Internet simulation data; PCA based multivariate anomaly detection algorithm; incremental PCA; incremental principal component analysis method; online network anomaly detection; time complexity; traffic matrix; incremental model; network anomaly detection; singular value decomposition(SVD);
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Information and Network Security (ICINS 2013), 2013 International Conference on
  • Conference_Location
    Beijing
  • Electronic_ISBN
    978-1-84919-729-8
  • Type

    conf

  • DOI
    10.1049/cp.2013.2472
  • Filename
    6826021