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Neural networks learning improvement using the k-means clustering algorithm to detect network intrusions

Auteurs: » FARAOUN Kamel Mohamed
Type : Revue Internationale
Nom du journal : International Journal of Computer and Information Engineering ISSN:
Volume : 1 Issue: 10 Pages: 3151-3158
Lien : »
Publié le : 28-10-2007

In the present work, we propose a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model use multi-layered network architecture with a back propagation learning mechanism. The K-means algorithm is first applied to the training dataset to reduce the amount of samples to be presented to the neural network, by automatically selecting an optimal set of samples. The obtained results demonstrate that the proposed technique performs exceptionally in terms of both accuracy and computation time when applied to the KDD99 dataset compared to a standard learning schema that use the full dataset.

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