Identifying Drug Traffickers on Encrypted Messaging Apps

With the increasing use of encrypted messaging applications, drug traffickers exploit these platforms for illegal transactions, making detection and enforcement challenging. This paper proposes a machine learning-based approach to identify drug traffickers by analysing communication patterns, metadata, and behavioural anomalies. The system employs Natural Language Processing (NLP), network analysis, and anomaly detection algorithms to flag suspicious activities while preserving user privacy. The proposed framework integrates real-time monitoring and automated alerting mechanisms, enhancing law enforcement’s ability to combat digital drug trafficking effectively.