Junqing Zhang

Junqing Zhang

A passionate wireless security researcher at University of Liverpool

Radio Frequency Fingerprint Identification Dataset

Published:

This paper summarizes Radio Frequency Fingerprint Identification research datasets (and code, if available) that are publicly available.

LoRa RFFI Dataset by University of Liverpool

  1. Guanxiong Shen, Junqing Zhang*, Xuyu Wang, and Shiwen Mao, “Federated Radio Frequency Fingerprint Identification Powered by Unsupervised Contrastive Learning,” IEEE Transactions on Information Forensics and Security, 2024.
  2. Guanxiong Shen, Junqing Zhang*, Alan Marshall, Roger Woods, Joseph Cavallaro, and Liquan Chen, “Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification”, IEEE Transactions on Mobile Computing, IEEE, arXiv link
  3. Guanxiong Shen, Junqing Zhang*, Alan Marshall, Mikko Valkama, and Joseph Cavallaro, “Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification,” IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2355 - 2367, Apr. 2023. IEEE, arXiv link
  4. Guanxiong Shen, Junqing Zhang*, Alan Marshall, and Joseph Cavallaro, “Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 774 - 787, Feb. 2022. IEEE, arXiv

GENESYS Lab at Northeastern University

GENESYS Lab at Northeastern University link has made several datasets available.

Oregon State University

The NetSTAR lab Oregon State University at Oregon State University has made a few datasets available, including LoRa and WiFi datasets. Dataset Download Link

LoRa Radio Data by Northeastern University and InterDigital

Al-Shawabka, A., Pietraski, P., Pattar, S.B., Restuccia, F., & Melodia, T. (2021, July 26-29). DeepLoRa: Fingerprinting LoRa Devices at Scale Through Deep Learning and Data Augmentation. Proc. 22nd International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Shanghai, China.

Drone Remote Controller RF Signal Dataset by North Carolina State University

  • M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, “Micro-UAV detection and classification from RF fingerprints using machine learning techniques,” in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
  • M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, “Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference,” IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
  • E. Ozturk, F. Erden, and I. Guvenc, “RF-based low-SNR classification of UAVs using convolutional neural networks.” arXiv preprint arXiv:2009.05519, Sept. 2020.
  • Dataset Download Link

ADS-B Dataset by Embry-Riddle Aeronautical University

Yongxin Liu, Jian Wang, Jianqiang Li, Shuteng Niu, and Houbing Song, “Class-Incremental Learning for Wireless Device Identification in IoT,” IEEE Internet of Things, vol. 8, no. 23, pp. 17227 - 17235, Dec. 2021.

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