Detecting Drug Trafficking On Encrypted Messaging Platforms

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Murali G; Madhusudan K J; Nagaraj; Naveen M; Asst. Prof. Mrs. Dhivya V

Detecting Drug Trafficking On Encrypted Messaging Platforms

Authors: Murali G, Madhusudan K J, Nagaraj, Naveen M, Asst. Prof. Mrs. Dhivya V

Unique Paper ID: 178322
Volume: 11
Issue: 12
PageNo: 5620-5626

Keywords: Telegram Bot Content Moderation Drug Trafficking Detection Real-Time Monitoring Keyword Detection User Geolocation FastAPI Neon DB Prisma ORM React Frontend WebSocket Integration Role- Based Dashboards Data Privacy Secure Authentication Data Encryption Machine Learning.

Abstract:
The increasing use of encrypted messaging platforms, such as Telegram, has created a significant challenge in monitoring and controlling harmful content, including drug trafficking activities. The Telegram Bot Monitoring System is designed to address this issue by leveraging real-time content analysis and user engagement to detect sensitive content, such as illegal drug references. This system utilizes advanced technologies including a Telegram Bot for keyword detection, FastAPI for backend services, Neon DB with Prisma ORM for efficient data management, and React for real-time user and admin dashboards. The bot actively scans messages in Telegram groups and private chats for predefined sensitive keywords associated with drug trafficking. Upon detection, the system prompts flagged users for additional contextual data, such as their geolocation, ensuring that administrators have the necessary information to evaluate potential threats. This real-time monitoring and user interaction workflow is critical for identifying illicit activities early and enabling administrators to take timely action. The system is designed to be scalable and secure, incorporating robust data privacy measures. It uses JWT for secure user authentication, bcrypt for password hashing, and ensures all data is encrypted both in transit and at rest. Role-based access control restricts sensitive information to authorized personnel only, ensuring compliance with privacy standards such as GDPR. Additionally, the system offers a modular architecture that can scale as the user base and message volume grow. Looking to the future, the Telegram Bot Monitoring System is poised for further enhancements, such as integrating machine learning algorithms for adaptive content detection, advanced natural language processing (NLP) for more accurate message analysis, and Kubernetes for efficient container orchestration. These improvements will help the system continuously evolve to meet the growing demands of online content moderation and provide a reliable solution for combating harmful digital activities.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 5620-5626

Detecting Drug Trafficking On Encrypted Messaging Platforms

Available:https://ijirt.org/article?manuscript=178322

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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