A new approach of network intrusion detection using HVDM-Based SOM

  • Authors:

  • Lei Wang;Yong Yang;Shixin Sun

  • Affiliations:

  • School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China;Suminet Communication Technology(Shanghai) Co., Ltd, Shanghai, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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Abstract

The research of applying Self-organizing Maps for intrusion detection is investigated in this paper. A novel approach is presented for enhancing SOM's abilities of identifying temporal network attacks, which combine with FIR filter. Meanwhile, we reconsider the heterogeneous dataset that composed of network connection's features, and select HVDM as the distance function determining the winning neuron during SOM's training and testing. In the end, KDD benchmark dataset is employed to validate the efficiency of our approach, and the results is detection rates of 96.5%, false positive rates of 6.2%, which accounts for good performance of our new approach in intrusion detection fields.