Aimed at the problems that the selection of kernel function and parameter adjustment in SVM algorithm are not scientific, and the detection is poor in accuracy of classification, a Grey Wolf Optimization Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm is proposed to improve the Intrusion Detection System (IDS) based on SVM. This method is to utilize PSOGWO algorithm for optimizing the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The optimal detection model of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments are made based on NSL-KDD dataset, and the results show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, improving significantly the detection rate, the convergence speed and the model balance. And this algorithm is feasible.