摘要
为了提高网络入侵检测的准确性与检测效率,弥补由单一优化算法带来的计算精度低、易陷入局部极值等不足,将差分算法的思想引入量子粒子群算法中,提出了一种改进量子粒子群算法(Improved Quantum Particle Swarm Optimization algorithm,IQPSO)和改进差分算法(Improved Difference Evolution,IDE)相融合的IQPSO-IDE算法,并将IQPSO-IDE算法对支持向量机(Support Vector Machine,SVM)的参数进行优化。以此为基础,设计了一种基于IQPSO-IDE算法的网络入侵检测方法。实验结果表明,IQPSO-IDE算法与传统的QPSO、GA-DE、QPSO-DE算法相比,不仅在效率上有了明显的改善,而且在网络入侵检测的正确率上分别提高了5.12%、3.05%、2.26%,在误报率上分别降低了3.31%、1.54%、0.93%,在漏报率上分别降低了1.26%、0.73%、0.52%。
In order to improve the testing efficiency and accuracy of network intrusion detection, and make up for the disadvantages of low computing precision and easy to get into local extremum caused by a single optimization algorithm,this paper introduces the idea of the difference algorithm into the quantum particle swarm algorithm, and proposes an IQPSO-IDE algorithm based on the Improved Quantum Particle Swarm Optimization algorithm(IQPSO)and the Improved Difference Evolution algorithm(IDE). In addition, the IQPSO-IDE algorithm also optimizes the parameters of support vector machines. Based on this, this paper designs a network intrusion detection method based on IQPSO-IDE algorithm. The experimental results show that the efficiency of the IQPSO-IDE algorithm is better than traditional QPSO algorithm, GA-DE algorithm, QPSO-DE algorithm, and the accuracy of network intrusion detection of this algorithm is increased by 5.12%, 3.05% and 2.26%, the rate of false positives is reduced by 3.31%, 1.54% and 0.93%, the non-response rate is decreased by 1.26%, 0.73% and 0.52%.
作者
马占飞
杨晋
金溢
边琦
MA Zhanfei;YANG Jin;JIN Yi;BIAN Qi(Baotou Teachers College, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014030, China;School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China;Vocational Skills Training Department, Inner Mongolia Normal University, Huhhot 010022, China)
出处
《计算机工程与应用》
CSCD
北大核心
2019年第10期115-120,204,共7页
Computer Engineering and Applications
基金
国家自然科学基金(No.61762071
No.61163025)
内蒙古自治区自然科学基金(No.2016MS0614)
内蒙古自治区高等学校科学研究基金(No.NJZY17287
No.NJZY201)
关键词
网络安全
入侵检测
量子粒子群算法
差分算法
支持向量机
network security
intrusion detection
quantum particle swarm optimization
differential evolution
support vector machine
