ML-Powered Behavior Analytics for Continuous Monitoring of Encrypted Enterprise Networks

The widespread adoption of encryption in enterprise networks has enhanced privacy but has simultaneously reduced the visibility required for effective security monitoring. As traditional inspection techniques become less viable, behavior-centric approaches offer a promising alternative for identifying malicious activity in encrypted environments. This study presents an ML-powered behavior analytics framework for continuous monitoring of encrypted enterprise traffic without violating confidentiality requirements. The proposed system models flow-level statistical attributes, temporal interaction patterns, and host-level behavioral baselines to distinguish routine operational activities from abnormal or high-risk events. A two-stage architecture featuring unsupervised learning for baseline construction and supervised learning for event classification was developed and assessed using large-scale encrypted traffic datasets representative of enterprise conditions. Experimental evaluation demonstrates that the framework achieves high detection performance with minimal false alarms and remains robust under evolving threat scenarios. The results underscore the effectiveness of machine-learning-driven behavioral analysis as a scalable, privacy-preserving strategy for continuous security monitoring in modern encrypted networks.