Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks Anwer Mustafa Hilal 1,2,* , Aisha Hassan Abdalla Hashim 1 , Heba G. Mohamed 3 , Lubna A. Alharbi 4 , Mohamed K. Nour 5 , Abdullah Mohamed 6 , Ahmed S. Almasoud 7 and Abdelwahed Motwakel 2 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: a.hilal@psau.edu.sa Received: 12 April 2022; Accepted: 22 June 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 gen- erated 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 cybersecur- ity (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 fea- ture extraction. In addition, the cascaded recurrent neural network (CRNN) model is applied for the identification and classification of CB. Finally, the SHO algo- rithm 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. Keywords: Cybersecurity; cyberbullying; online social network; deep learning; spotted hyena optimizer 1 Introduction Due to the expansion of the Internet, security is considered a significant factor. Though Web 2.0 offers interactive, simple, anywhere, and anytime accessibilities to the online societies, it additionally offers This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computer Systems Science & Engineering DOI: 10.32604/csse.2023.031181 Article ech T Press Science