Abstract: In order to solve the problem of the SVM intrusion detection method which has low detection rate, high disforting rate and slow detection speed, a kind of incremental SVM intrusion detection algorithm based on distance weighfed template reduction and the attribute information entropy was proposed. In this algorithm, the training sample set reduction is made according to the sample for the samples and the neighbors to the total distance weighted weight, then,the clustering sample point and the noise of the fitting point are taken out through the adjacent to the border area segmentation and based on sample attribute information entropy, and then, using the sample dispersion extracts possible support vector machine, and incremental learning based on KK I} conditions is made to construct the optimal SVM classifier. The simulation results show that the algorithm has good detection rate and the detection efficiency, and distorting rate low.

Key words: Intrusion detection, SVM, Weighted distance, Information entropy, Adjacent to the border area