A New Hybrid Approach Using GWO and MFO Algorithms to Detect Network Attack  

作　　者：Hasan Dalmaz Erdal Erdal Halil Muratünver 

机构地区：[1]Department of Computer Engineering,Faculty of Engineering and Architecture,Kırıkkale University,Kırıkkale,71450,Turkey

出　　处：《Computer Modeling in Engineering & Sciences》2023年第8期1277-1314,共38页工程与科学中的计算机建模（英文）

基　　金：supported by the Kırıkkale University Department of Scientific Research Projects (2022/022).

摘　　要：This paper addresses the urgent need to detect network security attacks,which have increased significantly in recent years,with high accuracy and avoid the adverse effects of these attacks.The intrusion detection system should respond seamlessly to attack patterns and approaches.The use of metaheuristic algorithms in attack detection can produce near-optimal solutions with low computational costs.To achieve better performance of these algorithms and further improve the results,hybridization of algorithms can be used,which leads to more successful results.Nowadays,many studies are conducted on this topic.In this study,a new hybrid approach using Gray Wolf Optimizer(GWO)and Moth-Flame Optimization(MFO)algorithms was developed and applied to widely used data sets such as NSL-KDD,UNSW-NB15,and CIC IDS 2017,as well as various benchmark functions.The ease of hybridization of the GWO algorithm,its simplicity,its ability to perform global optimal search,and the success of the MFO algorithm in obtaining the best solution suggested that an effective solution would be obtained by combining these two algorithms.For these reasons,the developed hybrid algorithm aims to achieve better results by using the good aspects of both the GWO algorithm and the MFO algorithm.In reviewing the results,it was found that a high level of success was achieved in the benchmark functions.It achieved better results in 12 of the 13 benchmark functions compared.In addition,the success rates obtained according to the evaluation criteria in the different data sets are also remarkable.Comparing the 97.4%,98.3%,and 99.2% classification accuracy results obtained in the NSL-KDD,UNSW-NB15,and CIC IDS 2017 data sets with the studies in the literature,they seem to be quite successful.

关 键 词：NETWORK attack detection HYBRID GWO MFO 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]