Open Access

ARTICLE

Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

by Anwer Mustafa Hilal^1,2,*, Aisha Hassan Abdalla Hashim¹, Heba G. Mohamed³, Lubna A. Alharbi⁴, Mohamed K. Nour⁵, Abdullah Mohamed⁶, Ahmed S. Almasoud⁷, Abdelwahed Motwakel²

1 Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur, 53100, Malaysia
2 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
3 Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Computer Science, College of Computers and Information Technology, Tabuk University, Tabuk, 47512, Saudi Arabia
5 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca, 24382, Saudi Arabia
6 Research Centre, Future University in Egypt, New Cairo, 11845, Egypt
7 Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 12435, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computer Systems Science and Engineering 2023, 45(2), 2033-2047. https://doi.org/10.32604/csse.2023.031181

Received 12 April 2022; Accepted 22 June 2022; Issue published 03 November 2022

Abstract

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

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Keywords

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