A Novel Intrusion Detection Model Based on Multi-layer Self-Organizing Maps and Principal Component Analysis

摘要

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.