AI-Driven Real-Time Phishing Detection System

ABSTRACT: The escalating threat of phishing attacks poses significant risks to individuals and organizations, compromising sensitive data and financial security. Traditional phishing detection methods, reliant on manual analysis and static rules, struggle to keep pace with the sophistication and volume of modern attacks. This study proposes an innovative artificial intelligence (AI)-driven system for real-time phishing detection, integrating machine learning algorithms with a dynamic network of data sources, including email metadata, URL features, and user behavior analytics. By leveraging advanced anomaly detection and predictive modeling, the system achieves enhanced accuracy and speed in identifying phishing attempts. Rigorous testing demonstrates that our approach outperforms conventional methods, achieving up to 65% faster detection and 95% accuracy in real-world scenarios. These findings underscore the potential of AI to revolutionize cybersecurity, providing timely insights for proactive defense and policy formulation.