Micallef, NicholasQasir, Yazeed2026-03-292025Qasir, Yazeed. Implementation of a Hybrid Phishing Detection Platform Using Machine Learning and Google Safe Browsing API. MSc dissertation, Swansea University, Department of Computer Science, 2025.https://hdl.handle.net/20.500.14154/78531MSc Cyber Security dissertation submitted to Swansea University, Department of Computer Science, September 29, 2025. The project presents a hybrid phishing detection platform combining Random Forest machine learning, Google Safe Browsing API integration, rule-based heuristics, and a web application for real-time URL assessment and user awareness.Phishing is a pervasive issue in cybersecurity, exploiting both technological weaknesses and human vulnerabilities to gain access to sensitive data. This dissertation introduces a hybrid phishing detection system using both a supervised Random Forest model and the Google Safe Browsing API to improve accuracy and adaptability to evolving attacks. The dataset, consisting of 247,950 URLs, was processed using lexical, domain-based, and content features, and the Random Forest model was trained on an 80/20 stratified split. The framework employs a layered architecture including allowlist checking, API verification, machine learning classification, and rule-based heuristics, whose outputs are combined to produce a final decision. Additionally, a web application was developed to provide real-time URL assessment and enhance user awareness through integrated educational features. Experimental results show that the API-only baseline achieved an ROC-AUC of 0.49, while the Random Forest model achieved an ROC-AUC of 0.993. The hybrid system significantly reduced false negatives while maintaining strong precision and recall. These findings demonstrate that combining API intelligence, machine learning, and user-focused interventions provides a scalable and effective approach to phishing detection.58enPhishing Detection Machine Learning Cybersecurity Google Safe Browsing API Random Forest URL ClassificationThesis