SafeHaven: A Secure Platform Leveraging Cyberbullying Detection, Morse Code, and Access Control

This paper explores the integration of advanced technologies to enhance user safety in digital applications, focusing on the limitations of existing models and systems to derive the innovative SafeHaven platform. It delves into the application of machine learning and transformer models for cyberbullying detection, and highlights the role of secure access controls such as facial recognition in safeguarding user interactions. The proposed SafeHaven platform combines a multi-layered authentication system using facial recognition and Morse code blinking, offering a secure and accessible alternative to traditional password systems. Additionally, a reputation-based scoring system built on the cyberbullying detection mechanism powered by BERT is incorporated to evaluate user behavior, reduce toxic interactions, and foster healthier online communities.