Encrypted Network Traffic Analysis for Real-Time Cybersecurity Threat Detection

This paper presents a real-time cybersecurity framework for Encrypted Network Traffic Analysis designed to detect malicious activity concealed inside encrypted communication channels. Modern enterprise networks rely heavily on encryption protocols such as TLS/SSL, HTTPS, QUIC, and VPN tunneling, rendering conventional deep-packet inspection methods ineffective. Threat actors now systematically abuse encryption to hide malware communications, data exfiltration pipelines, command-and-control (C2) channels, and advanced persistent threats. The proposed framework analyzes encrypted network flows without decrypting any payload, combining machine learning classification, unsupervised anomaly detection, TLS fingerprinting, behavioral flow modeling, and graph-based threat correlation to expose hidden malicious behavior. A real-time streaming pipeline, composite risk scoring engine, and explainable AI layer together deliver sub-300 ms threat detection with full decision transparency. Experimental evaluation demonstrates 94.2% classification accuracy, a ROC-AUC of 0.95, and the ability to process over 25,000 encrypted flows per second in a cloud-native Kubernetes environment. The solution is privacy-preserving, horizontally scalable, and designed for deployment in enterprise and government cybersecurity infrastructures.