基于多层自组织映射和主成分分析的入侵检测方法

Novel Intrusion Detection Method Based on Multi-layer Self-organizing Map and Principal Component Analysis

首先改进了自组织映射学习和分类算法,通过引入自定义变量匹配度、约简率和约简样本量化误差,提出了一种新的基于多层自组织映射和主成分分析入侵检测模型与算法。模型运用主成分分析算法对输入样本进行特征约简,运用分层思想对分类精度低的聚类进行逐层细分,解决了单层自组织映射分类不精确的问题。实验结果表明该模型用于入侵检测的效果良好,能准确区分攻击与否且能进一步指出攻击的具体类型。

The Self-Organizing Map （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.