Spotted hyena optimizer with deep learning driven cybersecurity for social networks

Mustafa Hilal, Anwer and Hassan Abdalla Hashim, Aisha and G. Mohamed, Heba and A. Alharbi, Lubna and K. Nour, Mohamed and Mohamed, Abdullah and S. Almasoud, Ahmed and Motwakel, Abdelwahed (2023) Spotted hyena optimizer with deep learning driven cybersecurity for social networks. Computer Systems Science and Engineering, 45 (2). pp. 2033-2047. ISSN 0267-6192

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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.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Cybersecurity; cyberbullying; online social network; deep learning; spotted hyena optimizer
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Aisha Hassan Abdalla Hashim
Date Deposited:	15 Dec 2022 09:11
Last Modified:	11 Dec 2023 14:00
URI:	http://irep.iium.edu.my/id/eprint/101889

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