Identifying Drug Traffickers on Encrypted Messaging Apps

Authors

  • R. Karthikeyan Assistant Professor, Department of Computer Science and Engineering, J.N.N Institute of Engineering, Kannigaipair, Tamil Nadu, India Author
  • Mopuru Deepika Department of Computer Science and Engineering, J.N.N Institute of Engineering, Kannigaipair, Tamil Nadu, India Author
  • Nelamala Yashwanth Department of Electronic Communication and Engineering, J.N.N Institute of Engineering, Kannigaipair, Tamil Nadu, India Author
  • Athikayala Pranay Kumar Yadav Department of Computer Science and Engineering, J.N.N Institute of Engineering, Kannigaipair, Tamil Nadu, India Author
  • Duvuru Mohith Reddy Department of Computer Science and Engineering, J.N.N Institute of Engineering, Kannigaipair, Tamil Nadu, India Author

DOI:

https://doi.org/10.32628/CSEIT25112818

Keywords:

Drug trafficking, encrypted messaging, machine learning, natural language processing, anomaly detection, network analysis, cybersecurity

Abstract

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.

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References

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Published

09-04-2025

Issue

Vol. 11 No. 2 (2025): March-April

Section

Research Articles

License

Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.