Intrusion detection method based on fuzzy hidden Markov model

  • Authors:

  • Yongzhong Li;Rushan Wang;Jing Xu;Ge Yang;Bo Zhao

  • Affiliations:

  • School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, P. R. China;School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, P. R. China;School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, P. R. China;School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, P. R. China;School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, P. R. China

  • Venue:
  • FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
  • Year:
  • 2009

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Abstract

Because of the excellent performance of the HMM (Hidden Markov Model), it has been widely used in pattern recognition. Due to the high false alarm rate in the classical intrusion detection system(IDS) based on HMM, a fuzzy approach for the HMM, called Fuzzy Hidden Markov Models (FHMM) is proposed. it is introduced with the Fuzzy logic to the HMM. The robustness and accurate rate of the IDS based on FHMM model are greatly improved. So a new intrusion detection method based on FHMM was proposed in this paper. The experiment results with 1998 DARPA data set shows that our method is efficiently to classify the anomaly profile from the normal profile, and has low false positive rate with high detection rate. Moreover, the training time is reduced, the detection speed is effectively increased and computer resources are saved.