Shen, Guanxiong, Zhang, Junqing ORCID: 0000-0002-3502-2926, Marshall, Alan ORCID: 0000-0002-8058-5242, Valkama, Mikko and Cavallaro, Joseph
(2021)
Radio Frequency Fingerprint Identification for Security in Low-Cost IoT
Devices.
In: 2021 55th Asilomar Conference on Signals, Systems, and Computers, 2021-10-31 - 2021-11-3.
Text
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Abstract
Radio frequency fingerprint identification (RFFI) can uniquely classify wireless devices by analyzing the received signal distortions caused by the intrinsic hardware impairments. The state-of-the-art deep learning techniques such as convolutional neural network (CNN) have been adopted to classify IoT devices with high accuracy. However, deep learning-based RFFI requires input data of a fixed size. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the low SNR RFFI is rarely investigated. In this paper, the state-of-the-art transformer model is used as the classifier, which can process signals of variable length. Data augmentation is adopted to improve low SNR RFFI performance. A multi-packet inference approach is further proposed to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low SNR RFFI performance by up to 50% and multi-packet inference can further increase it by over 20%.
Item Type: | Conference or Workshop Item (Unspecified) |
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Additional Information: | Asilomar 2021 |
Uncontrolled Keywords: | eess.SP, eess.SP |
Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
Depositing User: | Symplectic Admin |
Date Deposited: | 07 Apr 2022 13:14 |
Last Modified: | 18 Jan 2023 21:05 |
DOI: | 10.1109/ieeeconf53345.2021.9723287 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3152371 |