Unsupervised Wireless Diarization: A Potential New Attack on Encrypted Wireless Networks

• Published in: ICC 2023 - IEEE International Conference on Communications pp. 2312 - 2318
• Main Authors: Barati, C. Nicolas, Lamichhane, Bishal, Liao, Siyu, Graves, Eric, Swami, Ananthram, Sabharwal, Ashutosh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.05.2023
• Subjects: Neural networks; Pathology; Performance evaluation; Pipelines; Radio transmitters; Threat modeling; Wireless networks
• ISSN: 1938-1883
• DOI: 10.1109/ICC45041.2023.10278576

We present a new threat model enabling a passive adversary to infer which overheard packets belong to which transmitters. We call this threat model unsupervised wireless diarization (UWD) where the adversary assigns transmitter identity (label) to received packets in an encrypted wireless network without access to the MAC headers. To demonstrate the feasibility of such an attack, we develop UWDNet, a wireless diarization pipeline comprised of a Siamese neural network to extract embeddings from received packets, a similarity metric to compare embeddings, and unsupervised clustering. We evaluate UWDNet on both synthetic datasets and datasets of real wireless transmissions collected using Rice University's configurable massive MIMO testbed RENEW. Via various experimentation scenarios, our initial results show that UWDNet achieves a diarization accuracy of above 90% on synthetic data of transmitters it has never seen. To push the limits of performance evaluation, we collected a real radio transmissions dataset representing a worst-case (almost pathological) setting where all nodes are co-located. Even in this near-pathological case, UWDNet accuracy is > 60% - well above a random label assignment, indicating the feasibility of unsupervised wireless diarization in real-life scenarios. We also analyzed different factors such as the spatial channel and transmit parameters, which impact diarization accuracy in real-world scenarios.