AI-Driven Automated Phishing and Malware Detection Framework Using Deep Learning

Abstract

With the rapid evolution of artificial intelligence and digital connectivity, cyber threats have grown increasingly sophisticated, presenting severe security challenges to individuals and enterprises alike. Traditional signature-based detection mechanisms often fail against zero-day malware and dynamic phishing campaigns. This paper proposes an AI-driven automated detection framework leveraging machine learning and deep learning models to identify and neutralize phishing links and malicious software in real time. Comprehensive experimental evaluations demonstrate that the proposed architecture achieves exceptional accuracy, low latency, and minimal false-positive rates, significantly outperforming conventional security paradigms.