Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

作     者：Amani Abdulrahman Albraikan Siwar Ben Haj Hassine Suliman Mohamed Fati Fahd NAl-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel Manar Ahmed Hamza Mesfer Al Duhayyim 

作者机构：Department of Computer SciencesCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer ScienceCollege of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Information SystemsCollege of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer and Self-DevelopmentPreparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied SciencesCollege of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：907-923页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(GPR/303/42) Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R191),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：Cybersecurity cyberbullying social networks parameter tuning deep learning metaheuristics 

摘      要：Cyberbullying(CB)is a distressing online behavior that disturbs mental health *** studies have employed statistical and Machine Learning(ML)techniques for CB *** this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social *** proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter *** addition,GloVe approach is employed in the generation of word ***,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for ***,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)*** order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.