Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa

Shen, Guanxiong, Zhang, Junqing ORCID: 0000-0002-3502-2926, Marshall, Alan ORCID: 0000-0002-8058-5242 and Cavallaro, Joseph (2022) Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa. IEEE Transactions on Information Forensics and Security, 17. pp. 774-787.

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Official URL: http://dx.doi.org/10.1109/tifs.2022.3152404

Abstract

Radio frequency fingerprint identification (RFFI) is a promising device authentication technique based on the transmitter hardware impairments. In this paper, we propose a scalable and robust RFFI framework achieved by deep learning powered radio frequency fingerprint (RFF) extractor. Specifically, we leverage the deep metric learning to train an RFF extractor, which has excellent generalization ability and can extract RFFs from previously unseen devices. Any devices can be enrolled via the pre-trained RFF extractor and the RFF database can be maintained efficiently for allowing devices to join and leave. Wireless channel impacts the RFF extraction and is tackled by exploiting channel independent feature and data augmentation. We carried out extensive experimental evaluation involving 60 commercial off-the-shelf LoRa devices and a USRP N210 software defined radio platform. The results have successfully demonstrated that our framework can achieve excellent generalization abilities for device classification and rogue device detection as well as effective channel mitigation.

Item Type:	Article
Uncontrolled Keywords:	Feature extraction, Training, Spectrogram, Hardware, Authentication, Radio transmitters, Databases, Internet of things, device authentication, radio frequency fingerprint identification, deep learning, LoRa
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	04 Feb 2022 08:11
Last Modified:	15 Mar 2024 14:44
DOI:	10.1109/TIFS.2022.3152404
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3148161