A new model for dynamic intrusion detection

  • Authors:
  • Tao Li;Xiaojie Liu;Hongbin Li

  • Affiliations:
  • Department of Computer Science, Sichuan University, Chengdu, China;Department of Computer Science, Sichuan University, Chengdu, China;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ

  • Venue:
  • CANS'05 Proceedings of the 4th international conference on Cryptology and Network Security
  • Year:
  • 2005

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Abstract

Building on the concepts and the formal definitions of self, nonself, antigen, and detector introduced in the research of network intrusion detection, the dynamic evolution models and the corresponding recursive equations of self, antigen, immune-tolerance, lifecycle of mature detectors, and immune memory are presented. Following that, an immune-based model, referred to as AIBM, for dynamic intrusion detection is developed. Simulation results show that the proposed model has several desirable features including self-learning, self-adaption and diversity, thus providing a effective solution for network intrusion detection.