A Hybrid Approach for Phishing Email Detection using AI and Cryptography

Phishing emails remain a major threat to cybersecurity through the exploitation of human weaknesses and avoidance of traditional detection mechanisms. Current mechanisms such as AI-based models, rule-based systems, and cryptographic verifications each carry intrinsic limitations in containing dynamic phishing strategies. This paper presents an AI and cryptographic authentication-based hybrid phishing detection approach aimed at improving email security. The framework utilizes machine learning algorithms, i.e., Random Forest and XGBoost, to label emails according to headers, content, URLs, and attachments. In parallel, cryptographic protocols like SPF, DKIM, DMARC, S/MIME, and PGP validate sender identity and confirm data integrity. Through synergy among these methods, the hybrid technique reduces false positives and negatives, making the system resilient, adaptive, and scalable for practical implementations. The goal of this research is to present an overall solution bridging the loopholes in current detection schemes, providing better accuracy, flexibility, and security from advanced phishing attacks.