Published Paper Details:

In an increasingly digital world, the threat of phishing attacks through malicious URLs poses a significant risk to users and organizations alike. This project aims to develop an AI-powered detection system that utilizes machine learning algorithms to evaluate and assess the maliciousness of URLs. The system features a robust REST API and a user?friendly web application, allowing users to input URLs and receive AI-generated probabilities regarding their safety in real time. Additionally, a feedback mechanism is integrated into the application, enabling users to confirm or correct assessed probabilities, thus enhancing the accuracy and reliability of the URL analysis. This continuous feedback loop not only improves the machine-learning model's performance over time but also fosters user engagement and cybersecurity awareness. The backend architecture is designed to be scalable, accommodating increased user load and facilitating integration with existing cybersecurity solutions. Ultimately, this project contributes to a safer online environment by empowering users with the tools and knowledge needed to recognize and avoid phishing threats.