Phishing Site URL Detection System

Authors: Khushi Agarwal, Vimal Kartik, Professor Shubhi Verma

Abstract: The rapid growth of internet usage has led to a significant rise in phishing attacks, posing serious threats to user security and data privacy. Traditional detection methods, such as blacklist-based approaches, are often ineffective against newly emerging and sophisticated phishing websites. This research presents a Machine Learning-Based Phishing Website URL Detection System designed to identify malicious URLs in real time with high accuracy. The proposed system utilizes multiple machine learning algorithms and extracts key features from URL structures and domain characteristics to effectively distinguish between legitimate and phishing websites. It is integrated with a user-friendly web interface that enables instant URL analysis and prediction. Experimental results demonstrate that the system achieves reliable performance across diverse scenarios, providing a scalable and efficient solution for enhancing web security. The proposed approach reduces reliance on traditional methods and offers proactive protection against evolving phishing threats.

DOI: http://doi.org/