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

作     者：Hasan Dalmaz Erdal Erdal Halil Muratünver 

作者机构：Department of Computer EngineeringFaculty of Engineering and ArchitectureKırıkkale UniversityKırıkkale71450Turkey 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第136卷第8期

页      面：1277-1314页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：Network attack detection hybrid GWO MFO 

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 *** intrusion detection system should respond seamlessly to attack patterns and *** use of metaheuristic algorithms in attack detection can produce near-optimal solutions with low computational *** achieve better performance of these algorithms and further improve the results,hybridization of algorithms can be used,which leads to more successful ***,many studies are conducted on this *** 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 *** 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 *** these reasons,the developed hybrid algorithm aims to achieve better results by using the good aspects of both the GWO algorithm and the MFO *** reviewing the results,it was found that a high level of success was achieved in the benchmark *** achieved better results in 12 of the 13 benchmark functions *** addition,the success rates obtained according to the evaluation criteria in the different data sets are also *** 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.