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)


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.


Data fusion Fisher Scores RBF Nerve Network Network Intrusion detection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, Y., Li, S.: Multisensor Information Fusion and Its Application: A Survey. Control and Decision 16(5), 518–522 (2001)Google Scholar
  2. 2.
    Gao, X., Wang, Y.: Survey of Multisensor Information Fusion. Computer Automated Measurement & Control 10(11), 706–709 (2002)Google Scholar
  3. 3.
    Wan, C.H.: Self-configuring radial basis function neural networks for chemical pattern recognition. J. of Chemical Information and Computer Science 39(6), 1049–1056 (1999)CrossRefGoogle Scholar
  4. 4.
    Hall, D.L., Llinas, J.: An introduction to multi-sensor data fusion. Proc. IEEE 85(1), 6–23 (1997)CrossRefGoogle Scholar
  5. 5.
    Varshney, P.K.: Multisensor data fusion. J. Eclec. Commu. Eng. 9(6), 245–253 (1997)CrossRefGoogle Scholar
  6. 6.
    Antsaklis, P.J.: Neural Networks in Control Systems. Special Section on Neural Networks for Systems and Control IEEE Control System Magazine, 3–5 (1990)Google Scholar
  7. 7.
    Moody, J., Darken, C.: Fast Learning in Networks of Locally-tuned Processing Units. Neural Computation (1), 281–294 (1989)CrossRefGoogle Scholar
  8. 8.
    Inan, A., Kaya, S.V., Saygin, Y.: Privacy preserving clustering on horizontally partitioned data. Data & Knowledge Engineering 63(3), 646–666 (2007)CrossRefGoogle Scholar
  9. 9.
    Sing, J.K., Basu, D.K., Nasipuri, M., et al.: Self-Adaptive RBF Neural Network-Based Segmentation of Medical Images of the Brain. Proceedings of ICISIP 18(7), 447–452 (2005)Google Scholar
  10. 10.
    Wieland, A., Leighton, R.: Geometric Analysis of Neural Network Capacity. In: Proc. IEEE 1st ICN, vol. 1, pp. 385–392 (1987)Google Scholar
  11. 11.
    Jaakkola, T.S., Haussler, D.: Exploiting generative models in discriminative classifiers. In: Kearns, M.S., Solla, S.A., Coh, N.D.A. (eds.) Advances in Neural Information Processing Systems, vol. 11. MIT Press, Cambridge (1998)Google Scholar
  12. 12.
    Holub, A.D., Welling, M., Perona, P.: Combining generative models and Fisher kernels for object recognition. In: ICCV 2005, vol. 1(17-21), pp. 136–143. IEEE (2005)Google Scholar
  13. 13.
    KDD CUP 99. KDD Cup 99 dataset [EB/OL] (August 20, 2009),
  14. 14.
    Zou, Y., Wang, Y.: Implementation of Intrusion Detection System Based on Basis Function Neural Network. Journal of Guilin University of Electronic Technology 25(1), 48–50 (2005)Google Scholar

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

Personalised recommendations