A New Cryptographic Verification and Machine Learning Approach for Enhanced Email Security

Corporate email systems are frequent targets of cyber threats such as phishing, spoofing, and man-in-the-middle (MITM) attacks, posing significant risks to data integrity and security. This paper introduces the New Proposed Model (DrTT), which leverages Burrows-Abadi-Needham (BAN) logic to verify critical security properties in email communications. Through formal verification, the DrTT model ensures message integrity, authenticates the sender, and resists replay and MITM attacks-capabilities essential to mitigating phishing schemes. Comparative analysis with machine learning-based models, such as Random Forest and Deep Learning, reveals that these models lack the formal cryptographic guarantees required for robust protection, making them vulnerable to advanced phishing techniques. The DrTT model's integration of BAN logic and cryptographic methods sets a high standard in email security, underscoring the importance of formal verification for safeguarding corporate communication.