中文摘要：

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6.9119%,漏报率为9.7087%,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。

英文摘要：

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.