An RF fingerprint data enhancement method based on WGAN. (English) Zbl 07929668
Wang, Wei (ed.) et al., Communications, signal processing, and systems. Proceedings of the 12th international conference, September 6–8, 2023. Volume 1. Singapore: Springer. Lect. Notes Electr. Eng. 1032, 539-547 (2024).
Summary: RF fingerprinting is an emerging technology in the field of IoT security and is widely used in many areas, such as management, wireless device authentication, and interference source determination (Hall et al. in IEEE Trans Dependable Secure Comput 201-206, 2004 [1]; Wu et al. in Sci China Inf Sci 65(7):170304, 2022 [2]; Lin et al. in Sci China Inf Sci 2023 [3]). Most of these application scenarios rely on recognition methods for devices. Most of the mainstream recognition methods are based on a large amount of data for training. In case of insufficient sample size, the mainstream recognition methods are not applicable. Generative adversarial networks (GANs), with their adversarial properties, are well-suited and effective for applications in scenarios where the amount of data is insufficient. In this paper, we propose an RF fingerprint data enhancement method based on Wasserstein Generative Adversarial Network (WGAN). The experimental results show that the method can effectively improve the accuracy of RF fingerprint recognition in the same and limited data set.
For the entire collection see [Zbl 1537.94013].
For the entire collection see [Zbl 1537.94013].
MSC:
| 68Txx | Artificial intelligence |
References:
| [1] | Hall J, Barbeau M, Kranakis E (2004) Radio frequency fingerprinting for intrusion detection in wireless networks. IEEE Trans Dependable Secure Comput 201-206 |
| [2] | Wu, W.; Hu, S.; Lin, D.; Wu, G., Reliable resource allocation with RF fingerprinting authentication in secure IoT networks, Sci China Inf Sci, 65, 7, 2022 · doi:10.1007/s11432-021-3284-y |
| [3] | Lin, D.; Hu, S.; Wu, W.; Wu, G., Few-shot RF fingerprinting recognition for secure satellite remote sensing and image processing, Sci China Inf Sci, 2023 · doi:10.1007/s11432-022-3672-7 |
| [4] | Zou S, Liu J, Yang H (2013) Research of compensating zero-IF modulator IQ imbalance in digital domain. Video Eng 23:163-166. doi:10.16280/j.videoe.2013.23.030 |
| [5] | He Z, Hou S, Zhang W, Zhang Y (2021) Multi-feature fusion classification method for communication specific emitter identification. J Commun 02:103-112 |
| [6] | Shen, G.; Zhang, J.; Marshall, AJ; Cavallaro, J., Towards scalable and channel-robust radio frequency fingerprint identification for LoRa, IEEE Trans Inf Forensics Secur, 17, 774-787, 2021 · doi:10.1109/TIFS.2022.3152404 |
| [7] | Cekic M, Gopalakrishnan S, Madhow U (2020) Robust wireless fingerprinting: generalizing across space and time. arXiv:2002.10791 |
| [8] | Wang, W.; Gan, L., Radio frequency fingerprinting improved by statistical noise reduction, IEEE Trans Cogn Commun Netw, 8, 1444-1452, 2022 · doi:10.1109/TCCN.2022.3171792 |
| [9] | Wang T, Bian Y, Zhang Y, Hou X (2022) Using artificial intelligence methods to classify different seismic events. Seismol Res Lett |
| [10] | Shen G, Zhang J, Marshall AJ, Peng L, Wang X (2020) Radio frequency fingerprint identification for LoRa using spectrogram and CNN. In: IEEE INFOCOM 2021—IEEE conference on computer communications, pp 1-10 |
| [11] | Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. NIPS |
| [12] | Arjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networks. arXiv:1701.04862 |
| [13] | Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International conference on machine learning |
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