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 introduction of match-degree, reduction-rate quantification error reducing sample, a novel approach to intrusion detection based on Multi-layered modified SOM neural network Principal Component Analysis (PCA) is proposed. model, PCA applied feature selection, designed subdivide imprecise clustering in single-layered layer by layer. Experimental results demonstrate that model can provide precise efficient way for implementing classifier detection.