Detection Framework for Content-Based Cybercrime in Online Social Networks Using Metaheuristic Approach

In recent years, content-based cybercrime detection has become a topic of attraction among researchers. Cybercrime has emerged as a money-driven industry with malicious intent towards online social networks. Cyber-criminals aim to manipulate vulnerable areas in cyberspace by playing on human understanding and making a profit. They threaten minors, especially adolescents, who are not adequately overseen while online. To address this issue, there is an urgent need for a robust content-based cybercrime detection framework. The aim of this research work is to explore possible combinations of various preprocessing, feature selection and classification methodologies using the cuckoo search metaheuristic approach. This approach seeks to improve the performance of content-based cybercrime detection system. For the purpose of this research, four publicly available datasets for cyberbullying detection have been utilized for evaluating the effectiveness of the proposed algorithm. The algorithm was then further compared with three recent cyberbullying detection models based on various evaluation parameters. These parameters included precision, recall and f-measure. The experimental results demonstrate the effectiveness of the proposed approach. This approach outperformed other recent techniques on all the datasets, giving high predictive recall value via tenfold cross-validation.