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Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

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  • Kang Xie
  • Yixian Yang
  • Yang Xin
  • Guangsheng Xia

Abstract

According to the problems of current distributed architecture intrusion detection systems (DIDS), a new online distributed intrusion detection model based on cellular neural network (CNN) was proposed, in which discrete-time CNN (DTCNN) was used as weak classifier in each local node and state-controlled CNN (SCCNN) was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation which allows the distributed intrusion detection to be performed better.

Suggested Citation

  • Kang Xie & Yixian Yang & Yang Xin & Guangsheng Xia, 2015. "Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-10, March.
    • Handle: RePEc:hin:jnlmpe:343050
    DOI: 10.1155/2015/343050
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