LS-SVM Based Intrusion Detection using Kernel Space Approximation and Kernel-Target Alignment

For least squares support vector machines (LS-SVM) based intrusion detection method, there is a big obstacle that the amount of audit data for modelling is very large even for a small network scale, so it´s impractical to directly train LS-SVM using original training datasets. Furthermore, LS-SVM adopts equality constraints and squared functions, which leads to solve an easy-to-compute linear system, however followed is the lack of sparseness, all training data will become the support vector of LS-SVM, which cause the low intrusion detection speed. This paper proposed a novel LS-SVM intrusion detection model using kernel space approximation through greedy searching, thus constructed a subspace basis of original space populated by training data. Through this approximation, the training data was downsized and consequently the number of support vectors of ultimate LS-SVM model were reduced, which greatly helped to improve the response time of intrusion detection. The kernel-target alignment method was utilized to obtain optimal Gaussian kernel parameter and 10-fold cross-validate method to obtain optimal trade-off parameter. The MIT´s KDD Cup 99 dataset was used to evaluate our present model, and the results clearly demonstrate that the method can be an effective way for fast intrusion detection