An additive decision rules classifier for network intrusion detection

Authors:
Tommaso Pani;Francisco de Toro
Affiliations:
CITIC-Dpt. of Signal Theory, Telematics and Communications, University of Granada, Spain;CITIC-Dpt. of Signal Theory, Telematics and Communications, University of Granada, 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

This paper presents an additive decision rules binary classifier applied for network intrusion detection. The classifier is optimized by a multiobjective evolutionary algorithm in order to maximize both the classification accuracy and the coverage level (percentage of items that are classified, in opposite to items unable to be classified). Preliminary results provides very good accuracy in detecting attacks which make this relatively simple classifier very suitable to be applied in the studied domain.