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ABSTRACT: In this paper, the Self Organizing Maps (SOM) learning and classification algorithms are firstly modified. Then via the introduction of match-degree, reduction-rate and quantification error of reducing sample, a novel approach to intrusion detection based on Multi-layered modified SOM neural network and Principal Component Analysis (PCA) is proposed. In this model, PCA is applied to feature selection, and Multi-layered SOM is designed to subdivide the imprecise clustering in single-layered SOM layer by layer. Experimental results demonstrate that this model can provide a precise and efficient way for implementing the classifier in intrusion detection.
Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part III; 01/2006