Abstract: In order to improve the detection rate and reduce the training time, KFDA-SVM intrusion detection technology is proposed which combines the feature extraction technology and classification algorithm. In the proposed algorithm, the KFDA is used to extract the optimal discriminant vectors and then the SVM is adopted to classify the projected data. A mixture of kernels based HVDM is proposed according to the high dimensional and heterogeneous datasets acquired in the intrusion detection. Results of the experiment using KDD 99 indicate the effectiveness of the algorithm.

Key words: intrusion detection, kernel fisher discriminant analysis, support vector machine, mixture of kernels