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.
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Li, T., Liu, X., Li, H. (2005). A New Model for Dynamic Intrusion Detection. In: Desmedt, Y.G., Wang, H., Mu, Y., Li, Y. (eds) Cryptology and Network Security. CANS 2005. Lecture Notes in Computer Science, vol 3810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11599371_7
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DOI: https://doi.org/10.1007/11599371_7
Publisher Name: Springer, Berlin, Heidelberg
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