Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

作     者：Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour Abdullah Mohamed Ahmed S.Almasoud Abdelwahed Motwakel 

作者机构：Department of Electrical and Computer EngineeringInternational Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self DevelopmentPreparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical EngineeringCollege of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer ScienceCollege of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer SciencesCollege of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research CentreFuture University in EgyptNew Cairo11845Egypt Department of Information SystemsCollege of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第45卷第5期

页      面：2033-2047页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

基　　金：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:22UQU4310373DSR15 

主　　题：Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer 

摘      要：Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.