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Securing Network Traffic Using Genetically Evolved Transformations

Faraoun, Kamel Mohamed and Boukelif, Aoued (2006) Securing Network Traffic Using Genetically Evolved Transformations. Malaysian Journal of Computer Science, 19 (1). pp. 9-28. ISSN 0127-9084

Full text not available from this repository.

Official URL: http://mjcs.fsktm.um.edu.my/detail.asp?AID=349

Affiliations

Djillali Liabès University

Abstract

The 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 maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so becomes much easier. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficacy of a given interval repartition is handled by the fitness criterion, with maximum classes discrimination. Experiments were first performed using the Fisher’s Iris dataset, and the KDD’99 Cup dataset was used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and gives accepted results compared to other existing techniques.

Item Type:Journal
Keywords:Genetic programming, patterns classification, intrusion detection
Subjects:Q Science
ID Code:364

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