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Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/9916

Title: Neural networks learning improvement using the K-means clustering algorithm to detect network intrusions
???metadata.dc.creator???: Faraoun, K. M.
Boukelif, A.
Keywords: Neural networks
Intrusion detection
Learning enhancement
K-means clustering
Redes neurais
Detecção de intruso
Melhoria de aprendizagem
Agrupamento k-means
Publisher: Editora da UFLA
???metadata.dc.date???: 1-Sep-2006
Citation: FARAOUN, K. M.; BOUKELIF, A. Neural networks learning improvement using the K-means clustering algorithm to detect network intrusions. INFOCOMP: Journal of Computer Science, Lavras, v. 5, n. 3, p. 28-36, Sept. 2006.
Abstract: 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 backpropagation 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.
Other Identifiers: http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/140
???metadata.dc.language???: eng
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