Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

作     者：Ahmed S.Almasoud 

作者机构：Department of Information SystemsCollege of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第9期

页      面：2849-2863页

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：Prince Sultan University  PSU 

主　　题：Metaheuristics cyberattack detection machine learning cat swarm optimization security 

摘      要：The Internet of Things(IoT)is considered the next-gen connection network and is ubiquitous since it is based on the *** Detection System(IDS)determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified *** this back-ground,the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification(EMML-CADC)model in an IoT *** aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced *** attain this,the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform *** addition,Enhanced Cat Swarm Optimization based Feature Selection(ECSO-FS)approach is followed to choose the optimal feature ***,Mayfly Optimization(MFO)with Twin Support Vector Machine(TSVM),called the MFO-TSVM model,is utilized for the detection and classification of ***,the MFO model has been exploited to fine-tune the TSVM variables for enhanced *** performance of the proposed EMML-CADC model was validated using a benchmark dataset,and the results were inspected under several *** comparative study concluded that the EMML-CADC model is superior to other models under different measures.