• DocumentCode
    2732965
  • Title

    An improved intrusion detection system based on neural network

  • Author

    Han, Xiao

  • Author_Institution
    Sch. of Software & Microelectron., Peking Univ., Beijing, China
  • Volume
    1
  • fYear
    2009
  • fDate
    20-22 Nov. 2009
  • Firstpage
    887
  • Lastpage
    890
  • Abstract
    Neural network approach is an advanced methodology used for intrusion detection. Adaptive Resonance Theory (ART) is one kind of neural networks featuring self-organization of stable recognition categories and on-line learning. ART 2-A is a fast clustering algorithm of the ART family. But ART 2-A cannot process categorical values, while network traffic data collected for intrusion detection always contain this kind of data. An improved model of ART 2-A is thus proposed to deal with categorical data properly. To verify the accuracy of this new model, experiments were carried out on the KDD Cup 99 data set. Results showed that the performance of the new model was satisfactory.
  • Keywords
    ART neural nets; pattern clustering; security of data; ART 2-A clustering algorithm; KDD Cup 99 data set; adaptive resonance theory; intrusion detection system; neural network; online learning; stable recognition category; Clustering algorithms; Feedforward neural networks; Intrusion detection; Microelectronics; Neural networks; Protection; Resonance; Subspace constraints; Telecommunication traffic; Traffic control; information security; intrusion detection; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-4754-1
  • Electronic_ISBN
    978-1-4244-4738-1
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2009.5358048
  • Filename
    5358048