构造稀疏最小二乘支持向量机的网络入侵检测模型

Construct Sparse Least Squares Support Vector Machine for Network Intrusion Detection

从最小二乘支持向量机的稀疏表达出发,构建高效的基于稀疏最小二乘支持向量机的网络入侵检测模型,提出了一种通过基于核空间近似策略的有效低秩逼近来有效减小原始训练样本集中的支持向量数来实现最终模型的稀疏表达。以MIT KDD99数据集为基础,对所提出方法进行有效性验证,并与利用剪枝策略通过递归过程中不断减少模型中支持向量个数的稀疏化方法、基本最小二乘支持向量机以及标准支持向量机方法的性能进行对比。结果表明:基于核空间近似的最小二乘支持向量机稀疏化与标准最小二乘支持向量机相当;此外稀疏最小二乘支持向量机能够提高入侵检测响应速度。

From the viewpoint of sparseness representation building of least squares support vetor machine（LSSVM）, a novel sparse LS-SVM is presented for the modeling of network intrusion detection. The proposed sparse LS-SVM may be constructed via two methods, i. e. , the iteration elimination according to the sorted value of model coefficients; the kernel space approximation method to construct the low rank subset approximation of training dataset that is applied for the training of LS-SVM to achieve sparse- ness and to improve the intrusion detection response speed. The proposed sparse LS-SVM is illustrated via MIT KDD 99 dataset and the results show that a better performance can be achieved in comparsion to LSSVM.