Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model  

作　　者：Hanan Abdullah Mengash Jaber S.Alzahrani Majdy M.Eltahir Fahd N.Al-Wesabi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk 

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]Department of Industrial Engineering,College of Engineering at Alqunfudah,Umm Al-Qura University,Saudi Arabia [3]Department of Information Systems,College of Science&Art at Mahayil,King Khalid University,Saudi Arabia [4]Department of Computer Science,College of Science&Art at Mahayil,King Khalid University,Saudi Arabia [5]Research Centre,Future University in Egypt,New Cairo,11745,Egypt [6]Department of Computer and Self Development,Preparatory Year Deanship,Prince Sattam bin Abdulaziz University,AlKharj,Saudi Arabia [7]Department of Mathematics,Faculty of Science,Cairo University,Giza,12613,Egypt

出　　处：《Computer Systems Science & Engineering》2023年第5期1393-1407,共15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 under Grant Number(RGP 2/158/43);Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R114),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,etc.Despite the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)approaches.This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN model.The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites.The SRO-MLCOSN model initially employs Glove technique for word embedding process.Besides,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of CB.Finally,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM model.The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

关 键 词：CYBERSECURITY CYBERBULLYING social networking machine learning search and rescue optimization 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]