An Incremental SVM for Intrusion Detection Based on Key Feature Selection

Proposed a method of detecting intrusion using incremental SVM based on key feature selection. A center SVM summarizes the distributed samples and incorporates them to build the incremental SVM for locals. By eliminating the redulldant features of sample dataset the space dimension of the sample data is reduced. Using this method it can overcome the shortages of SVM-time-consuming of training and massive dataset storage. The simulation experiments with KDD Cup 1999 data demonstrate that our proposed method achieves the increasing performance for intrusion detection.