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Towards an artificial immune system for network intrusion detection: An investigation of dynamic clonal selection

Kim, J; Bentley, PJ; (2002) Towards an artificial immune system for network intrusion detection: An investigation of dynamic clonal selection. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002. (pp. 1015 - 1020).

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Abstract

One significant feature of artificial immune systems is their ability to adapt to continuously changing environments, dynamically learning the fluid patterns of 'self' and predicting new patterns of 'non-self'. This paper introduces and investigates the behaviour of dynamiCS, a dynamic clonal selection algorithm, designed to have such properties of self-Adaptation. The effects of three important system parameters: tolerisation period, activation threshold, and life span are explored. The abilities of dynamiCS to perform incremental learning on converged data, and to adapt to novel data are also demonstrated. © 2002 IEEE.

Type:Proceedings paper
Title:Towards an artificial immune system for network intrusion detection: An investigation of dynamic clonal selection
DOI:10.1109/CEC.2002.1004382
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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