Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

被引：0

作者：

Mengash H.A. ^{[1
]}

Alzahrani J.S. ^{[2
]}

Eltahir M.M. ^{[3
]}

Al-Wesabi F.N. ^{[4
]}

Mohamed A. ^{[5
]}

Hamza M.A. ^{[6
]}

Marzouk R. ^{[7
]}

机构：

[1] Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh

[2] Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University

[3] Department of Information Systems, College of Science & Art at Mahayil, King Khalid University

[4] Department of Computer Science, College of Science & Art at Mahayil, King Khalid University

[5] Research Centre, Future University in Egypt, New Cairo

[6] Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj

[7] Department of Mathematics, Faculty of Science, Cairo University, Giza

来源：

Computer Systems Science and Engineering | 2023年 / 45卷 / 02期

关键词：

cyberbullying; Cybersecurity; machine learning; search and rescue optimization; social networking;

D O I：

10.32604/csse.2023.030328

中图分类号：

学科分类号：

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. © 2023 CRL Publishing. All rights reserved.

引用

页码：1393 / 1407

页数：14

共 21 条

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