URL PHISHING DETECTION SYSTEM USING MACHINE LEARNING
Srinath Chiranjeevi · International Scientific Journal of Engineering and Management · 2025
ABSTRACT Phishing websites are an increasing cybersecurity threat, which deceives users into providing sensitive information. This paper proposes a machine learning-based phishing detection system that checks URL-based attributes to ascertain the legitimacy of a website. The model is developed with a Gradient Boosting Classifier (GBC) and is trained on a dataset with 30 extracted features, which provides an accuracy rate of 97.4%. A web application based on Flask is created to enable real-time URL analysis so that users can check website safety effectively. The system provides more accurate detection and ease of use than conventional methods. Future enhancement involves integrating real-time web crawling and sophisticated deep learning methods to further enhance phishing detection. Keywords: Phishing detection, machine learning, URL analysis, GBC,web crawling.