Citation :

Anitha Mareedu, 2025. "Machine Learning for Secure Network Traffic Analysis: From Flow Classification to Encrypted Threat Detection" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 3, Issue 2: 64-74.

Abstract :

The growing adoption of encryption protocols such as TLS 1.3, QUIC, and DNS-over-HTTPS has limited the effectiveness of traditional deep packet inspection, challenging conventional methods of network traffic analysis. In response, machine learning (ML) has emerged as a powerful alternative, enabling the analysis of encrypted and obfuscated traffic through side-channel features, flow metadata, and behavioral patterns. This review systematically examines the evolution of ML-based techniques for secure network traffic analysis, covering supervised flow classification, anomaly detection, and encrypted threat inference. We analyze key components such as feature extraction strategies, learning models, and benchmark datasets, and assess the effectiveness of ML-powered network intrusion detection systems (NIDS) in operational settings. Tools like Zeek, CICFlowMeter, and Suricata extensions are discussed in the context of practical deployment. Furthermore, the review addresses emerging challenges including data privacy, adversarial robustness, and model explainability. We conclude by identifying open research directions focused on integrating ML with threat intelligence, enhancing interpretability, and enabling scalable, privacy-preserving detection in modern enterprise environments.

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Keywords :

Machine Learning (ML), Encrypted Traffic Analysis, Intrusion Detection (NIDS), TLS 1.3, QUIC, Federated Learning, Explainable AI (XAI).