基于核学习的入侵检测改进方法

Improved Intrusion Detection Method Based on Kernel Learning

针对入侵检测中部分攻击类型检测率低的问题,提出一种基于核学习的入侵检测改进方法。采用核主成分分析(KPCA)对入侵检测中的高维非线性结构数据集进行数据预处理,通过支持向量数据描述(SVDD)构造分类器,对预处理后的数据进行分类。实验结果表明,与已有方法相比,改进方法的检测精度较高、漏检率较低。

Due to low detection rate about some attack types in intrusion detection, this paper presents an improved intrusion detection method based on kernel learning. Because of high dimensional and nonlinear structure dataset in intrusion detection, Kernel Principal Component Analysis（KPCA） is presented to preprocess the dataset. The classifier is developed by Support Vector Data Description（SVDD）, and the data through preprocessed is applied in the classifier. Experimental results show that the improved method can acquire higher detection precision and lower missed detection rate.