改进的最小二乘支持向量机在入侵检测系统中的应用

Application of Improved Least Squares Support Vector Machine in Intrusion Detection System

介绍最小二乘支持向量机的基本原理论,提出基于最小二乘支持向量机的网络入侵检测系统模型。由于计算经验风险的损失函数为二次函数形式,LSSVM丧失了标准支持向量机的稀疏性,为使LSSVM具有稀疏性,从统计分析的角度出发,应用主成分分析的方法,对样本集进行特征提取,消除变量间的相关性,选取训练样本中分类作用最大的若干样本个体作为支持向量,并将非支持向量上的分类信息转移至支持向量上,提出新的LSSVM稀疏化算法——基于主成分分析的最小二乘支持向量机算法。实验结果表明,由此构建的稀疏LSSVM分类器保持了支持向量机的良好分类性能,而稀疏率相对高,其支持向量数甚至少与标准支持向量机,明显提高了LSSVM的分类效率和实时性。

Introduces the relative theory of the LSSVM, puts forward a network intrusion detection model based on the LSSVM. Since the empirical risk is calculated via quadratic function, LSSVM loses sparseness of SVM. To spare LSSVM, the principal component analysis method is used to extract feature, clear up the irrelevance and select import examples of training sample as support vector （SV）, and the information of non SV examples was transformed to SV, so new sparse algorithm was proposed, PCA-LSSVM. The result shows that the sparse LSSVM classifier keeps the classify ability of the SVM, and the sparse rate is higher, the SV count is less than standard support vector, enhance the classify efficiency and the real-time of the LSSVM obviously.