SVM algorithm based on sample density and its application in network intrusion detection
基于样本密度的SVM及其在入侵检测中的应用

When the network dataset is very large,conventional Support Vector Machine(SVM)learning algorithm is remarkably slow.By contrast,the proposed algorithm based on space block and sample density is fast.It was applied in intrusion detection in this paper.The algorithm selects training samples by local sample density,to reduce the training samples and thus to improve the speed of learning.Simulation shows that the algorithm is faster than the techniques of intrusion detection based on conventional SVM while it guarantees the high classification precision.