TY - GEN
T1 - Unsupervised Wireless Diarization
T2 - 2023 IEEE International Conference on Communications, ICC 2023
AU - Barati, C. Nicolas
AU - Lamichhane, Bishal
AU - Liao, Siyu
AU - Graves, Eric
AU - Swami, Ananthram
AU - Sabharwal, Ashutosh
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85178274338&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178274338&partnerID=8YFLogxK
U2 - 10.1109/ICC45041.2023.10278576
DO - 10.1109/ICC45041.2023.10278576
M3 - Conference contribution
AN - SCOPUS:85178274338
T3 - IEEE International Conference on Communications
SP - 2312
EP - 2318
BT - ICC 2023 - IEEE International Conference on Communications
A2 - Zorzi, Michele
A2 - Tao, Meixia
A2 - Saad, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 May 2023 through 1 June 2023
ER -