改进粒子群算法和支持向量机的网络入侵检测

网络入侵检测一直是网络安全领域中的研究热点,针对分类器参数优化难题,为了提高网络入侵检测准确性,提出一种改进粒子群算法和支持向量机相融合的网络入侵检测模型(IPSO-SVM).首先将网络入侵检测率作为目标函数,支持向量机参数作为约束条件建立数学模型,然后采用改进粒子群算法找到支持向量机参数,最后采用支持向量机作为分类器建立入侵检测模型,并在Matlab 2012平台上采用KDD 999数据进行验证性实验.结果表明,IPSO-SVM解决了分类器参数优化难题,获得更优的网络入侵分类器,提高网络入侵检测率,虚警率和漏报率大幅度下降.

Network Intrusion Detection Based on Improved Particle Swarm Optimization Algorithm and Support Vector Machine

Network intrusion detection is a hot research topic in network security, in order to improve the accuracy of network intrusion detection, a network intrusion detection model (IPSO-SVM) is proposed based on improved particle swarm optimization algorithm and support vector machine to solve the problem of classifier''s parameters optimization. Firstly, network intrusion detection rate is taken as the objective function, and support vector machine parameters are used as the constraint conditions to establish mathematical model, and secondly improved particle swarm optimization algorithm is used to find the optimal parameters, finally, support vector machine is used as classifier to build intrusion detection model, and KDD 1999 data is used to validate the performance in Matlab 2012. The results show that IPSO-SVM has solved the optimization problem of the classifier''s parameters and improved detection rate, reduced false alarm rate, false negative rate of the network intrusion.