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The Research on Fisher-RBF Data Fusion Model of Network Security Detection

  • Jian Zhou
  • Juncheng Wang
  • Zhai Qun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)

Abstract

Based on the artificial neural network and means of classification, this paper puts forward the Fisher-RBF Data Fusion Model. Abandon redundant and invalid data and decrease dimensionality of feature space to attain the goal of increasing the data fusion efficiency. In the simulation, the experiment of the network intrusion detection is conducted by using KDDCUP’99_10percent data set as the data source. The result of simulation experiment shows that on a fairly large scale, Fisher-RBF model can increase detection rate and discrimination rate, and decrease missing-report rate and misstatement rate.

Keywords

Data fusion Fisher Scores RBF Nerve Network Network Intrusion detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jian Zhou
    • 1
  • Juncheng Wang
    • 2
  • Zhai Qun
    • 3
  1. 1.Center of Information and NetworkHefei University of TechnologyHefeiChina
  2. 2.School of Computer & InformationHefei University of TechnologyHefeiChina
  3. 3.School of Foreign StudiesHefei University of TechnologyHefeiChina

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