Research on network intrusion detection based on BPMFO algorithm
白天毅 1王建国1
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作者信息
1. 西安工业大学,西安 710021
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摘要
随着网络技术的快速发展,网络入侵也越发频繁,而传统网络入侵检测技术存在偏差高、过早收敛等问题.因此,研究提出了一种基于二进制飞蛾扑火优化算法(Binary Moth-Flame Optimization integrated with Particle swarm optimization,BPMFO)的网络入侵检测模型.首先,通过对网络数据进行预处理,可以提取出代表网络入侵行为的特征;其次,通过BPMFO算法对特征进行优化;最后,利用飞蛾扑火算法(Moth-Flame Optimization,MFO)、飞蛾扑火优化算法(Moth-Flame Optimization integrat-ed with Particle swarm optimization,PMFO)和BPMFO在已知的攻击数据集中进行对比.结果表明,采用BPMFO算法可以有效地提高网络入侵检测的精度和效率.
Abstract
With the rapid development of network technology,network intrusion has become more frequent,and the traditional network intrusion detection techniques have problems such as high bias and premature convergence.Therefore,the study proposes a network intrusion detection model based on the Binary Moth-Flame Optimization integrated with Particle swarm optimization(BPMFO)algorithm.Firstly,by prepro-cessing the network data,the features representing the network intrusion behavior can be extracted.Second-ly,the features are optimized by BPMFO algorithm.Finally,using Moth-Flame Optimization(MFO),Moth-Flame Optimization integrated with Particle swarm optimization,PMFO)and BPMFO in the known at-tack dataset for comparison.The results show that the use of BPMFO algorithm can effectively improve the accuracy and efficiency of network intrusion detection.
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