Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification

Shen, Guanxiong, Zhang, Junqing ORCID: 0000-0002-3502-2926, Marshall, Alan ORCID: 0000-0002-8058-5242, Valkama, Mikko and Cavallaro, Joseph R (2023) Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 18. pp. 2355-2367.

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Official URL: http://dx.doi.org/10.1109/tifs.2023.3266626

Abstract

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, state-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is often unsatisfactory. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and transformer. We adopt data augmentation during training which can significantly improve the model's robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged 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 classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.

Item Type:	Article
Uncontrolled Keywords:	Internet of Things, LoRa, LoRaWAN, device authentication, radio frequency fingerprint, deep learning
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	28 Mar 2023 07:59
Last Modified:	15 Mar 2024 14:44
DOI:	10.1109/TIFS.2023.3266626
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169265