Hybrid Machine Learning and Language Model Approach for Monitoring and Regulating International Dark Web Transactions

The rise of illicit transactions on the dark web poses significant challenges to international security and financial regulation. A hybrid approach integrating machine learning and language models (ML-LM) offers a robust solution for monitoring and regulating these transactions. Existing methods primarily rely on rule-based detection and traditional machine learning techniques, which struggle with evolving encryption techniques, multilingual content, and disguised transactions. These limitations hinder effective detection and classification of illicit activities. To address these challenges, we propose an ML-LM framework that combines supervised and unsupervised machine learning models with advanced natural language processing (NLP) techniques. The system leverages deep learning-based anomaly detection, sentiment analysis, and multilingual text processing to identify suspicious transactions and patterns with high accuracy. Additionally, entity recognition and contextual understanding enhance transaction classification. The proposed approach enables law enforcement and financial regulators to monitor and analyze dark web transactions in real time, improving response time and intervention strategies. This system is scalable and adaptable to emerging threats. Experimental results demonstrate that the ML-LM framework significantly improves transaction classification accuracy, reduces false positives, and enhances the detection of hidden patterns in illicit activities. This approach provides a more effective and automated method for combating dark web financial crimes globally.