Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

• Published in: Computers, materials & continua materials & continua, 2022, Vol.72 (1), p.907-923
• Main Authors: Abdulrahman Albraikan, Amani, Ben Haj Hassine, Siwar, Mohamed Fati, Suliman, N. Al-Wesabi, Fahd, Mustafa Hilal, Anwer, Motwakel, Abdelwahed, Ahmed Hamza, Manar, Al Duhayyim, Mesfer
• Format: Article
• Language: English
• Subjects: Algorithms; Bullying; Classification; Comparative studies; Cyberbullying; Deep learning; Machine learning; Mental health; Motivation; Optimization; Recurrent neural networks; Social networks
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2022.024488

Cyberbullying (CB) is a distressing online behavior that disturbs mental health significantly. Earlier studies have employed statistical and Machine Learning (ML) techniques for CB detection. With this motivation, the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification (ODL-CDC) technique for CB detection in social networks. The proposed ODL-CDC technique involves different processes such as pre-processing, prediction, and hyperparameter optimization. In addition, GloVe approach is employed in the generation of word embedding. Besides, the pre-processed data is fed into Bidirectional Gated Recurrent Neural Network (BiGRNN) model for prediction. Moreover, hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization (SRO) algorithm. In order to validate the improved classification performance of ODL-CDC technique, a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects. A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques, in terms of performance, with the maximum accuracy of 92.45%.