• DocumentCode
    2691163
  • Title

    Intrusion Detection Based on RBF Neural Network

  • Author

    Bi, Jing ; Zhang, Kun ; Cheng, Xiaojing

  • Author_Institution
    Beijing Univ. of Civil Eng. & Archit., Beijing, China
  • fYear
    2009
  • fDate
    16-17 May 2009
  • Firstpage
    357
  • Lastpage
    360
  • Abstract
    Radial basis function (RBF) has been one of the most common neural networks used in the intrusion detection system (IDS). To improve the approximation performance and calculation speed of RBF, we describe a method to deal with the benchmark datasets adopted in the research. It includes converting the string to numeric elements firstly, then omitting the unnecessary data and ensuring that the data has the reasonable range limit. The simulation results built upon Matlab software show that the RBF neural network has better performance than BP neural network.
  • Keywords
    mathematics computing; radial basis function networks; security of data; Matlab software; RBF neural network; approximation performance; intrusion detection; radial basis function; Civil engineering; Computer networks; Data mining; Electronic commerce; Event detection; Feedforward neural networks; Intrusion detection; Military standards; Neural networks; Radial basis function networks; Intrusion Detection; Network Security; RBF network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Engineering and Electronic Commerce, 2009. IEEC '09. International Symposium on
  • Conference_Location
    Ternopil
  • Print_ISBN
    978-0-7695-3686-6
  • Type

    conf

  • DOI
    10.1109/IEEC.2009.80
  • Filename
    5175137