Published Paper Details:

Email communication is a critical component of modern professional and personal interactions, yet it remains one of the most exploited channels for cyberattacks such as phishing, malware infiltration, and social engineering. Conventional spam filters, operating primarily at the user device level, often fail to provide proactive defense and adaptive learning, resulting in exposure to malicious emails and compromised data security. This project presents the design and development of Sentinel - a Gateway-Level Intelligent Email Security System, capable of real-time analysis and classification of incoming emails using Natural Language Processing (NLP) and Machine Learning (ML) models. The system integrates an AI-based filtering engine with a secure web gateway, ensuring that malicious emails are quarantined before they reach the user's inbox. Continuous data feedback from users is analyzed by a retraining module, enabling adaptive improvement of the classifier's precision. The framework also includes a web-based dashboard and cloud connectivity for centralized monitoring, feedback visualization, and scalable deployment across enterprises and individuals.