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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Bai, J., Wu, Y., Wang, G., Yang, S.X., Qiu, W. (2006). A Novel Intrusion Detection Model Based on Multi-layer Self-Organizing Maps and Principal Component Analysis. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_37
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DOI: https://doi.org/10.1007/11760191_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
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