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| Content Provider | ACM Digital Library |
|---|---|
| Author | Quax, Peter Marin, Eduard Lamotte, Wim Preneel, Bart Robyns, Pieter Singelée, Dave |
| Abstract | Physical-layer fingerprinting investigates how features extracted 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 different vendors can be fingerprinted with 99% to 100% accuracy per symbol. The fingerprinting can be performed using only inexpensive commercial off-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. |
| Starting Page | 58 |
| Ending Page | 63 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450350846 |
| DOI | 10.1145/3098243.3098267 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-07-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Fingerprinting Lora Phy layer |
| Content Type | Text |
| Resource Type | Article |
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