Anomaly Detection and Prevention in SMTP

Emails are a vital part of communication in organizations, but they are increasingly at risk of threats like data breaches and cyberattacks. Protecting sensitive information shared through emails is essential for maintaining data security and complying with regulations. However, manually checking emails for anomalies or confidential information can be tedious and impractical, especially in large-scale setups.To address this issue, we propose a system that detects and classifies email anomalies in real time. The system uses advanced machine learning algorithms to analyze email content and sending patterns, effectively identifying unusual activities. By leveraging a pre-trained dataset like the Enron email dataset, it compares email features to detect anomalies. Additionally, it integrates with an SMTP server using Python libraries, allowing it to monitor outgoing emails and flag any suspicious behavior in email-sending patterns.This system provides real-time oversight, ensuring that threats are quickly identified and managed. With a simple and intuitive interface, users can easily monitor and address flagged emails, enabling proactive security measures. In the end, this solution strengthens email security, ensures compliance with data protection rules, and creates a more secure and trustworthy communication environment.