• DocumentCode
    3312603
  • Title

    Network Intrusion Detection Method Based on High Speed and Precise Genetic Algorithm Neural Network

  • Author

    Tian, Jingwen ; Gao, Meijuan

  • Author_Institution
    Dept. of Autom. Control, Beijing Union Univ., Beijing
  • Volume
    2
  • fYear
    2009
  • fDate
    25-26 April 2009
  • Firstpage
    619
  • Lastpage
    622
  • Abstract
    Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of neural network, an intrusion detection method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the network intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.
  • Keywords
    backpropagation; genetic algorithms; neural nets; security of data; BP network; floating-point code genetic algorithm; intrusion behaviors; network intrusion detection method; neural network; Artificial intelligence; Artificial neural networks; Computer networks; Convergence; Genetic algorithms; Information security; Intrusion detection; Neural networks; Uncertainty; Wireless communication; Network; genetic algorithm; intrusion detection; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networks Security, Wireless Communications and Trusted Computing, 2009. NSWCTC '09. International Conference on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-1-4244-4223-2
  • Type

    conf

  • DOI
    10.1109/NSWCTC.2009.228
  • Filename
    4908545