Intrusion detection model based on LISOMAP relevant vector machine

Abstract

Concerning low classification accuracy and high false alarm rate of current intrusion detection models,an intrusion detection classification model based on Landmark ISOmetric MAPping(LISOMAP) and Deep First Search(DFS) Relevant Vector Machine(RVM) was proposed.The LISOMAP was adopted to reduce the dimension of the training data,and RVM based on the DFS was used for classification detection.Compared with the Principal Components Analysis(PCA)-Supported Vector Machine(SVM),the experimental results indicate that the LISOMAP-DFSRVM model has lower false alarm rate with almost the same detection rate.