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http://dx.doi.org/10.6109/jkiice.2021.25.9.1271

GAN based Data Augmentation of Channel Data for the Application of RF Finger-printing in NFC  

Lee, Woongsup (Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.9, 2021 , pp. 1271-1274 More about this Journal
Abstract
RF fingerprinting based on deep learning (DL) has gained interests as a means to improve the security of near field communication (NFC) by allowing identification of NFC tags based on unique physical characteristics. To achieve high accuracy in the identification of NFC tags, it is crucial to utilize a large number of training data, however it is hard to collect such dataset in practice. In this study, we have provided new methodology to generate RF waveform from NFC tags, i.e., data augmentation, based on a conditional generative adversarial network (CGAN). By using the RF waveform of NFC tags which is collected from the testbed with software defined radio (SDR), we have confirmed that the realistic RF waveform can be generated through our proposed scheme.
Keywords
Generative adversarial network (GAN); RF finger printing; NFC communication; Deep learning; Data augmentation;
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