PHISHNET: Intelligence System for Phishing Attacks Using Machine Learning

Abstract-- Phishing attacks remain a significant threat to cybersecurity, targeting individuals and organizations alike. This paper introduces PHISHNET, a comprehensive threat intelligence system that leverages machine learning techniques to detect and mitigate phishing attacks across two communication vectors: URLs, emails. The system employs Gradient Boosting for URL detection, achieving an accuracy of approximately 98%, while Random Forest algorithms are utilized for email, attaining accuracies of approximately 97% and 96%, respectively. By analyzing various features related to URLs and extracting content from emails, PHISHNET provides users with actionable insights to enhance their security posture against phishing threats. Keywords - Feature Extraction, URL Analysis, Real-Time Detection, Cybersecurity, Building Classification, Machine Learning