Abstract: An intrusion detection method based on SVM combined with PSO is proposed. The global search characteristic of PSO is used to search for the best SVM’s parameter：[C]and[σ], and mutation operation is introduced in PSO in order to obtain?globally optimal solutions. After finding the optimal [C]and[σ], training and testing operation of intrusion detection system based on PSO_SVM are performed. It has high real-time and accuracy. The simulation results show that the detection rate is 92.8%, false alarm is 6.9119%and losing alarm is 9.7087%. It verifies the effectiveness and feasibility of the proposed algorithm.

Key words: intrusion detection, support vector machines, particle swarm optimization, network security

摘要： 提出基于粒子群优化（Particle Swarm Optimization，PSO）算法和支持向量机（Support Vector Machines，SVM）的入侵检测方法，为优化SVM性能，使用PSO的全局搜索特性寻找SVM的最优参数[C]和[σ]；为避免PSO算法陷入局部最优，引入变异操作，找到最优参数组合后进行基于PSO_SVM入侵检测算法的训练和检测，解决了入侵检测系统准确度难题。仿真实验表明该方法的检测率为92.8%，误报率为6.911 9%，漏报率为9.708 7%，对KDDCUP竞赛的最佳结果有一定程度的提高，实验结果验证了该算法的有效性和可行性。

关键词: 入侵检测, 支持向量机, 粒子群算法, 网络安全