Dark Web Intelligence Model for Cybercrime Prevention: A Case Study of Cyber Fraud - Johnson Sunday Osasona; Boniface Kayode Alese (Journal of Information Security System and Cyber Criminology Research, 2026)
Cyber fraud conducted through dark web platforms continues to pose significant challenges for cybersecurity monitoring and digital investigations. This study presents a dark web intelligence framework that identifies and analyzes fraudulent cyber activity by integrating natural language processing, network analysis, and machine learning. The framework processes unstructured data from underground forums and anonymized communication channels through data preprocessing, feature engineering, and supervised learning. Naive Bayes and random forest algorithms were implemented for fraud classification, while Bayesian risk inference was applied to prioritize threat severity and enhance analytical interpretation. In addition, interaction-based network analysis was utilized to identify influential actors and communication patterns associated with cyber fraud operations. Experimental results indicate that the framework successfully identified multiple fraud-related activities and extracted meaningful threat indicators, achieving strong classification performance. However, reduced performance was observed in categories with overlapping semantic characteristics, indicating the need for improved contextual feature representation. The framework achieved macro F1-scores above 0.92 during fraud classification and further generated intelligence-driven outputs, including automated alerts and high-risk actor identification, to support proactive cybercrime investigation and prevention efforts. The proposed approach provides a scalable, interpretable, and intelligence-driven solution for dark web cyber threat analysis and contributes to ongoing research in cybersecurity intelligence operations.