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 | Source: | 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. | Keywords: | security and privacy; mobile and wireless security; networks; network privacy and anonymity | Document URI: | http://hdl.handle.net/1942/24046 | Link to publication/dataset: | https://www.esat.kuleuven.be/cosic/publications/article-2765.pdf | ISBN: | 9781450350846 | DOI: | 10.1145/3098243.3098267 | ISI #: | 000628530300007 | Rights: | © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2022 vabb 2020 |
Appears in Collections: | Research publications |
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robyns2017physical.pdf | Published version | 929.26 kB | Adobe PDF | View/Open |
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