Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model  

作　　者：Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki 

机构地区：[1]Department of Computer Science,College of Science&Art atMahayil,King Khalid University,Muhayel Aseer,62529,Saudi Arabia [2]Department of Electrical Engineering,College of Engineering,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [3]Department of Computer Sciences,College of Computing and Information System,Umm Al-Qura University,Saudi Arabia [4]Department of Computer Science,College of Sciences and Humanities-Aflaj,Prince Sattam bin Abdulaziz University,Saudi Arabia [5]Department of Digital Media,Faculty of Computers and Information Technology,Future University in Egypt,New Cairo,11835,Egypt [6]Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University,Al-Kharj,16278,Saudi Arabia [7]Department of Information System,College of Computer Engineering and Sciences,Prince Sattam bin Abdulaziz University,AlKharj,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第5期1679-1694,共16页计算机系统科学与工程（英文）

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(45/43);Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140);Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR16).

摘　　要：Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication restrictions.Since traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find beneficial.This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)model.The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment.At the preliminary stage,data pre-processing is carried out to transform the input data into useful format.In addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection process.Besides,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of cyberattacks.Finally,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization process.The experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several aspects.The simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关 键 词：Cyberattacks SECURITY deep learning internet of things feature selection data classification 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]