Abuse Reporting System
Sreeram Sivadasan, Mohamed Jisar C, Rasmina, Murshida Farsana, K Shijaz · 2024
This project embarks on the development of an abuse reporting system designed to maintain user anonymity and employ advanced machine learning algorithms for genuineness assessment and fraud detection. In the contemporary digital landscape, safeguarding user privacy and ensuring the reliability of abuse reports are paramount. Our project addresses this challenge by integrating state-of-the-art machine learning techniques, enhancing the accuracy and efficiency of abuse reporting process, and mitigating false or malicious reporting. The technical underpinnings of the system involve the creation of a distributed, scalable, and fault-tolerant architecture capable of handling a wide range of abuse reports from diverse sources. We implement advanced natural language processing (NLP) models for textbased report analysis, while image and multimedia reports undergo deep learning-based content analysis. Anonymity preser-vation is achieved through advanced encryption techniques and decentralized data storage. Furthermore, we integrate adaptive machine learning algorithms that continuously evolve to detect novel abuse patterns and emerging fraud strategies, ensuring the system's adaptability to evolving threats. The project includes a comprehensive evaluation of the abuse reporting app's performance, precision, and scalability in handling a large volume of reports while preserving user anonymity. This project represents a significant advancement in the field of abuse reporting systems, offering a technologically robust and privacy-centric solution for contemporary society's increasing demand for secure and effective reporting mechanisms.