一种基于IPSO-SVM算法的网络入侵检测方法

摘要

Network intrusion detection has always been the research focus in the field of computer network security,and the current network is facing many potential security problems.In order to improve the accuracy of network intrusion detection,this paper improved the particle swarm optimization (PSO) algorithm,and then optimized the parameters of support vector machine (SVM) by using the improved PSO algorithm.On this basis,this paper also designed a novel network intrusion detection method based on IPSO-SVM algorithm.The experiment results show that the proposed IPSO-SVM algorithm is efficient.Compared with the classical SVM algorithm and PSO-SVM algorithm,IPSO-SVM algorithm not only improves the convergence speed of the network training obviously,but also improves the accuracy rate of network intrusion detection by 7.78％ and 4.74％ respectively,decreases the false positive rate by 3.37％ and 1.19％,and decreases the false negative rate by 1.46％ and 0.66％.%网络入侵检测一直是计算机网络安全领域的研究热点,当前网络面临着诸多的安全隐患.为了提高网络入侵检测的准确性,首先对粒子群优化(Particle Swarm Optimization,PSO)算法进行了改进,然后利用改进的PSO算法(IPSO算法)对支持向量机(Support Vector Machine,SVM)的参数进行了优化,并在此基础上设计了一种新型的基于IPSO-SVM算法的网络入侵检测方法.实验结果表明,相比于经典的SVM和PSO-SVM算法,IPSO-SVM算法不仅明显改善了网络训练的收敛速度,而且其网络入侵检测的正确率分别提高了7.78％和4.74％,误报率分别降低了3.37％和1.19％,漏报率分别降低了1.46％和0.66％.