Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment  

作　　者：Faisal SAlsubaei Haya Mesfer Alshahrani Khaled Tarmissi Abdelwahed Motwakel 

机构地区：[1]Department of Cybersecurity,College of Computer Science and Engineering,University of Jeddah,Jeddah,21959,Saudi Arabia [2]Department of Information Systems,College of Computer and Information Sciences,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,Makkah,24211,Saudi Arabia [4]Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University,AlKharj,16242,Saudi Arabia

出　　处：《Intelligent Automation & Soft Computing》2023年第6期2897-2914,共18页智能自动化与软计算（英文）

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R237);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:(22UQU4331004DSR13).

摘　　要：Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of cyberattacks.The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process.Furthermore,Android malware is increasing on a daily basis.So,precise malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile devices.In this research article,an optimal Graph Convolutional Neural Network-based Malware Detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud environment.The pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud platforms.The presented OGCNN-MDC model has three stages in total,such as data pre-processing,malware detection and para-meter tuning.To detect and classify the malware,the GCNN model is exploited in this work.In order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

关 键 词：CYBERSECURITY IoT CLOUD malware detection graph convolution network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] G841[文化科学—体育训练]