Authors - Chetana Shravage, Shubhangi Vairagar, Priya Metri, Shreya C. Jaygude, Pradnya P. Sonawane, Pradnya D. Kudwe, Siddheshwari S. Patil Abstract - This project presents an innovative phishing detection system that addresses the limitations of traditional methods by combining URL-based and content- based features to accurately identify fraudulent websites. Unlike conventional approaches that rely heavily on blacklisting and heuristics, which struggle with zero-day attacks and frequent updates, this system employs machine learning algorithms to automatically extract and analyze critical features from URLs and webpage content. By leveraging a comprehensive dataset that consists of fraudulent (phishing) websites along with legitimate websites, the system aims to improve detection rates to optimize performance based on evaluation metrics like accuracy, precision, F-1 score, recall, and false-positive rates. The system makes use of selective machine learning models like Random Forest, Decision Tree, and Support Vector Machine (SVM), which provide the benefit of increased scalability, robustness and improved effectiveness in phishing detection. Ultimately, this project aims to deliver a scalable, real-time detection solution that effectively mitigates phishing threats in a rapidly evolving landscape.
