An Evolutionary Computation Based Classification Model for Network Intrusion Detection

Abstract

Current techniques used for network intrusion detection have limited capabilities in coping with the dynamic and increasingly complex nature of security threats. In this paper, we propose a classification model for detecting intrusions based on Genetic Programming, Artificial Immune Recognition Systems (AIRS1, AIRS2), and Clonal Selection Algorithm (CLONALG). Further, six Rank based, viz., Information Gain, Gain ratio, Symmetrical Uncertainty, Chi squared Attribute Evaluator, Relief-F, and one-R; and five search based feature selection methods, viz., PSO Search, Genetic Search, Best First Search, Greedy Stepwise, and Rank Search have been employed to select the most relevant attributes before classification. The performance of the model has been evaluated in terms of accuracy, precision, detection rate, F-value, false alarm rate, and fitness value.