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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24046

Title: Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning
Authors: Robyns, Pieter
Marin, Eduard
Lamotte, Wim
Quax, Peter
Singelée, Dave
Preneel, Bart
Issue Date: 2017
Publisher: ACM Press
Citation: Proceedings of the 10th ACM Conference on Security & Privacy in Wireless and Mobile Networks, ACM Press,p. 58-63
Abstract: from radio signals can be used to uniquely identify devices. This paper proposes and analyses a novel methodology to fingerprint LoRa devices, which is inspired by recent advances in supervised machine learning and zero-shot image classification. Contrary to previous works, our methodology does not rely on localized and low-dimensional features, such as those extracted from the signal transient or preamble, but uses the entire signal. We have performed our experiments using 22 LoRa devices with 3 different chipsets. Our results show that identical chipsets can be distinguished with 59% to 99% accuracy per symbol, whereas chipsets from di erent vendors can be ngerprinted with 99% to 100% accuracy per symbol. The fingerprinting can be performed using only inexpensive commercial on-the-shelf software defined radios, and a low sample rate of 1 Msps. Finally, we release all datasets and code pertaining to these experiments to the public domain.
URI: http://hdl.handle.net/1942/24046
Link to publication: https://www.esat.kuleuven.be/cosic/publications/article-2765.pdf
DOI: 10.1145/3098243.3098267
ISBN: 9781450350846
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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