Owing to the constraints of time and space complexity,the standard SVM（Support Vector Machine） algorithm cannot effectively deal with large-scale network intrusion detection.In order to solve this problem and in view of the geometric interpretation of SVM,an intrusion detection classification algorithm named PCH-SVM is proposed based on the parallel convex hull decomposition and the SVM.With the help of convex hull decomposition and parallel computing,this algorithm can fast extract the vertices of convex hull of the original training samples to build a reduced SVM training set.Experimental results show that the proposed algorithm can effectively reduce the time and space complexity during SVM training,and speeds up the modeling and detection of intrusion detection classifier without any accuracy loss.