A multi-objective evolutionary algorithm for network intrusion detection systems

Authors:
J. Gómez;C. Gil;R. Baños;A. L. Márquez;F. G. Montoya;M. G. Montoya
Affiliations:
Dpt. Languages and Computation, Univ. of Almería, Almería, Spain;Dpt. Computer Architecture and Electronics, Univ. of Almería, Almería, Spain;Dpt. Computer Architecture and Electronics, Univ. of Almería, Almería, Spain;Dpt. Computer Architecture and Electronics, Univ. of Almería, Almería, Spain;Dpt. Rural Engineering, Univ. of Almería, Almería, Spain;Dpt. Computer Architecture and Electronics, Univ. of Almería, Almería, Spain
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

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Abstract

Attacks against computer systems are becoming more complex, making it necessary to develop new security systems continually, such as Intrusion Detection Systems (IDS) which provide security for computer systems by distinguishing between hostile and non-hostile activity. With the aim of minimizing the number of wrong decisions of a misuse (signature-based) IDS, an optimization strategy for automatic rule generation is presented. This optimizer is a Pareto-based multi-objective evolutionary algorithm included within a network IDS, which has been evaluated using a benchmark dataset. The results obtained show the advantages of using this multi-objective approach.