Intrusion Detection Based on Kernel Fisher Discriminant Analysis and Minimax Probability Machine Classifier

To improve the performance of Minimax Probability Machine (MPM) in the detection rate and the training time, Intrusion Detection Based on Kernel Fisher Discriminant Analysis and Minimax Probability Machine Classifier (KFDA-MPM) algorithm is proposed which combines the feature extraction technology and classification algorithm. In this method, the KFDA is used to extract the optimal feature set and then the MPM is adopted to classify the optimization data. Results of the experiment using the Knowledge Discovery and Data Mining Cup 1999 (KDDCUP99) datasets indicate the effectiveness of the algorithm.