Employing Machine Learning Algorithms to Detect Phishing URL Websites

Phishing attacks have risen like wildfire in the era of new digital technology in the wake of exploiting holes in the electronic communication process while cheating a user to seek private information. This work provides machine learning-based resistance against phishing attacks by differentiating phishing websites using recognition of patterns and analysis on data feature. By training several machine learning models, including Random Forest, XGBoost, and Support Vector Machine (SVM), on a dataset that included characteristics frequently found on phishing websites, we developed a system that could distinguish accurately between genuine websites and phishing attempts. This project brought in a nice innovation through the incorporation of these predictive models into a Flask-developed web application, thus allowing for an interactive, real-time phishing detection. The project shall ensure protection of financial security and personal information by giving users an easy interface to input website links so they can get instant feedback about whether the link is likely to be legitimate. This hybrid technique improves accessibility to non-technical users, brings better detection accuracy, and makes phishing detection more proactive, approachable, and successful in reality.