标准支持向量机（SVM）算法受时间和空间复杂度约束,无法有效地处理大规模网络入侵检测问题.文中基于SVM的几何解释,提出了一种基于并行凸包分解计算和支持向量机的入侵检测分类算法（PCH-SVM）.该算法借助凸包的分解和并行计算快速提取训练样本空间几何凸包的顶点,构建约简SVM训练样本集.实验结果表明,该算法可以在不造成精度损失的前提下,降低SVM训练的时空复杂度,加速入侵检测分类器的建模和检测.

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.