A novel hybrid KPCA and SVM with GA model for intrusion detection
A novel method combination of KPCA and SVM is proposed for intrusion detection. KPCA is used as a preprocessor of SVM to extract features. A new radial basis kernel function (N-RBF) is developed for KPCA and SVM. GA is employed to optimize the parameters of SVM. Experimental results shown that the proposed method had more excellent performance.
intrusion detection kernel function kernel principal component analysis support vector machines genetic algorithm