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DOE OSTI · 2496593

Transformer Masked Autoencoders for RF Device Fingerprinting

Parpart, Gavin G. [BATTELLE (PACIFIC NW LAB)]·Tu, Jonathan H. [BATTELLE (PACIFIC NW LAB)]·Clymer, Bradley J. [BATTELLE (PACIFIC NW LAB)]·Lee, Jung H. [BATTELLE (PACIFIC NW LAB)]·Babcock, Jasen T. [BATTELLE (PACIFIC NW LAB)]
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Abstract

Machine learning methods for RF device fingerprinting typically rely on CNN-based models. Transformer-based models have outperformed CNNs for modulation classification tasks, but there are few implementations for device fingerprinting. We train a transformer for device fingerprinting with the largest device count to date and explore several variations of the architecture. Additionally, we demonstrate that pre-training an RF transformer as a Masked Autoencoder improves classification accuracy, as has been observed for CNN fingerprinting models and vision transformers.

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Parpart, Gavin G. [BATTELLE (PACIFIC NW LAB)], Tu, Jonathan H. [BATTELLE (PACIFIC NW LAB)], Clymer, Bradley J. [BATTELLE (PACIFIC NW LAB)], Lee, Jung H. [BATTELLE (PACIFIC NW LAB)], Babcock, Jasen T. [BATTELLE (PACIFIC NW LAB)]. 2024-12-06. Transformer Masked Autoencoders for RF Device Fingerprinting. https://doi.org/10.1109/milcom61039.2024.10773654

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