Genetic Programming Approach for Multi-Category Pattern Classification Applied to Network Intrusions Detection

Kamel Faraoun¹ and Aoued Boukelif²

¹Evolutionary Engineering and Distributed IS Laboratory, University of Sidi Bel Abbès, Algeria

²Communication Networks, Architectures, and Multimedia Lab, University of Sidi Bel Abbès, Algeria 

Abstract: This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so become much easier. Contrary to the existing GP-classification techniques, the proposed one use a dynamic repartition of the transformed data in separated intervals, the efficacy of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were first performed using the Fisher’s Iris dataset, and then, the KDD’99 Cup dataset was then used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperform the existing GP-classification methods, and give a very accepted results compared to other existing techniques.  

Keywords: Genetic programming, patterns classification, intrusion detection.