改进PSO算法的支持向量机在NIDS中的应用

支持向量机(SVM )作为一种分类技术已经成功运用于入侵检测，但是支持向量机的
性能与参数的选择相关。在实际应用中支持向量机的参数选取问题一直没有得到很好地解决。粒子群优化（PSO）算法作为一种基于群智能方法的进化计算技术，具有良好的全局搜索能力。为了能够自动获取最优的支持向量机参数，提出了在入侵检测系统中基于改进微粒群优化(IPSO)算法的支持向量机参数选择方法，以kdd99数据集进行了仿真实验。仿真实验结果表明：基于粒子群训练的支持向量机方法能够比较好地提高入侵检测系统中数据的分类精度。
关键词：粒子群优化；支持向量机；入侵检测系统
Abstract : As a classification technical , support vector machines（SVM）have been applied in intrusion detection successful, But the performance of SVM is determined by its hyper parameters. In practice, the problem on how to select parameters of SVM is not solved properly. As an evolutionary computation technique based on swarm intelligence particle swarm optimization (PSO) algorithm has high global search ability, In order to optimize parameters of SVM automatically, a parameter selection approach based on PSO is proposed in this paper. Experiments with the data set kdd99 show that the method which based on PSO training of the SVM can improve the classification accuracy of dataset in IDS.
Key words: Particle swarm optimization; support vector machines; Intrusion detection system