Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection  

作　　者：Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza 

机构地区：[1]Department of Information Systems,College of Computer and Information Sciences,Princess Nourah bint Abdulrahman University,P.O.Box 84428,Riyadh,11671,Saudi Arabia [2]Prince Saud Al Faisal Institute for Diplomatic Studies,Riyadh,Saudi Arabia [3]Department of Industrial Engineering,College of Engineering at Alqunfudah,Umm Al-Qura University,Saudi Arabia [4]Department of Information Technology,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif,21944,Saudi Arabia [5]Department of Information Systems,College of Computer and Information Sciences,King Saud University,Saudi Arabia [6]Department of Computer Science,College of Computers and Information Technology,University of Bisha,Saudi Arabia [7]Department of Computer Science,Faculty of Computers and Information Technology,Future University in Egypt,New Cairo,11835,Egypt [8]Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University,AlKharj,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第6期3037-3052,共16页计算机系统科学与工程（英文）

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R319);Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia;the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR41.

摘　　要：Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to data.But the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so on.Presently,malware attacks and software piracy pose serious risks in compromising the security of IoT.They can steal confidential data which results infinancial and reputational losses.The advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud environment.This article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud Net-works.The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment.The proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification problem.The EAG-TODL-CTD model pre-processes the input data to transform to a compatible for-mat.For threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class labels.Finally,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our work.The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign.The experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关 键 词：CYBERSECURITY computer networks threat detection internet of things cloud computing deep learning 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]