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A Modified RBF Neural Network for Network Anomaly Detection

  • Xiaotao Wei
  • Houkuan Huang
  • Shengfeng Tian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

A modified RBF (radial basis function)-based neural network is proposed for network anomaly detection. Special attention is given to the determination of the parameters of the hidden layer. We propose a novel grid-based approach to compress and cluster the training data. The number, center and radii of the RBFs are determined according to the clustering result. At the detecting stage, we expand each input node with a sigmoid function to meet the type of input data. Experimental result on KDD 99 intrusion detection datasets shows that our RBF based IDS has high detection rate while maintaining a low false positive rate. It also shows the remarkable ability of our IDS to detect new type of attacks.

Keywords

Training Data Hide Layer Radial Basis Function Hide Node Intrusion Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Yang, Z.M., Wei, X.M., Bi, L.Y., Shi, D.P., Li, H.: An Intrusion Detection System Based in RBF Neural Network. In: Shen, W.-m., Chao, K.-M., Lin, Z., Barthès, J.-P.A., James, A. (eds.) CSCWD 2005. LNCS, vol. 3865, pp. 873–875. Springer, Heidelberg (2006)Google Scholar
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    Jiang, J., Zhang, C.L., Kamel, M.: RBF-based Real-time Hierarchical Intrusion Detection Systems. In: Proceedings of the International Joint Conference on Neural Networks, Ore-gon, USA, pp. 1512–1516 (2003)Google Scholar
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    Wang, W., Yang, J., Muntz, R.R.: Sting: A Statistical Information Grid Approach to Spatial Data Mining. In: Proceedings of the 23rd International Conference on Very Large Data Bases, Athens, Greece, pp. 186–195 (1997)Google Scholar
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    KDD Cup 1999 Data. Machine Learning for Intrusion Detection Project. The University of Columbia (2000), http://www.cs.columbia.edu/ids/ml

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaotao Wei
    • 1
  • Houkuan Huang
    • 1
  • Shengfeng Tian
    • 1
  1. 1.School of software, School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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