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

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dc.citation.firstpage2355en_US
dc.citation.journalTitleIEEE Transactions on Information Forensics and Securityen_US
dc.citation.lastpage2367en_US
dc.citation.volumeNumber18en_US
dc.contributor.authorShen, Guanxiongen_US
dc.contributor.authorZhang, Junqingen_US
dc.contributor.authorMarshall, Alanen_US
dc.contributor.authorValkama, Mikkoen_US
dc.contributor.authorCavallaro, Joseph R.en_US
dc.date.accessioned2023-07-18T18:47:26Zen_US
dc.date.available2023-07-18T18:47:26Zen_US
dc.date.issued2023en_US
dc.description.abstractRadio 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%.en_US
dc.identifier.citationShen, Guanxiong, Zhang, Junqing, Marshall, Alan, et al.. "Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification." <i>IEEE Transactions on Information Forensics and Security,</i> 18, (2023) IEEE: 2355-2367. https://doi.org/10.1109/TIFS.2023.3266626.en_US
dc.identifier.doihttps://doi.org/10.1109/TIFS.2023.3266626en_US
dc.identifier.urihttps://hdl.handle.net/1911/114944en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsThis work is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the work beyond the bounds of Fair Use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.titleToward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identificationen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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