Intrusion Detection Based on Cross-Validation SVM

Aiming at the problem of the higher rates of missing alarm and false alarm in the traditional intrusion detection system,we applied support vector machine(SVM) to intrusion detection,and proposed a novel method which introduced cross-validation in the learning procedure of SVM.Using the radial basis function(RBF) as the core,the training set was divided into several subsets,and each subset was tested by using the classifier obtained by training the other subset.The best two parameters of RBF were got and applied to the final classifier.Experimental results show that,with this method,intrusion attacks can be detected effectively and precisely,it has higher rate of detection and more generalization capability,at the same time,it has lower false alarm rate and miss rate.The method can be applied to intrusion detection system effectively.