Biblio

In recent works, numerous physical-layer security systems have been proposed as alternatives to classic cryptography. Such systems aim to use the intrinsic properties of radio signals and the wireless medium to provide confidentiality and authentication to wireless devices. However, fundamental vulnerabilities are often discovered in these systems shortly after their inception. We therefore challenge the assumptions made by existing physical-layer security systems, and postulate that weaker assumptions are needed in order to adapt for practical scenarios. We also argue that if no computational advantage over an adversary can be ensured, secure communication cannot be realistically achieved.

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.