Machine Learning Based Traffic Analysis in Heterogeneous and Federated Networks
This research focuses on developing traffic analysis techniques that can restore network visibility despite the widespread adoption of encryption and increasingly complex, distributed network architectures. The work addresses three key scales: device-level fingerprinting of IoT devices in encrypted environments, infrastructure-level analysis of radio access networks undergoing continuous updates, and detection/characterization of federated learning traffic patterns.
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