Radio Frequency Fingerprint Identification Dataset
Published:
This paper summarizes Radio Frequency Fingerprint Identification research datasets that are publicly available.
LoRa RFFI Dataset by University of Liverpool
We have made the dataset and code for our paper open source.
LoRa RFFI Dataset 1
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
The dataset is accessible from IEEE Dataport and the code can be downloaded from github.
LoRa RFFI Dataset 2
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
The dataset is accessible from IEEE Dataport and the code can be downloaded from github.
GENESYS Lab at Northeastern University
GENESYS Lab at Northeastern University link has made several datasets available.
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. [Paper presentation]. 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.
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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