Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

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.

Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction

“…Although it is apparent that social media platforms offer numerous benefits to their users, these platforms may also be exploited for malevolent purposes [2,8,9]. Cyberbullying is a prominent and profound example of these malevolent trends [10]. Specifically, the pervasiveness of these social media platforms has undeniably sparked, advanced, and worsened bullying among its users [11].…”