Comprehensive RF Dataset Collection and Release: A Deep Learning-Based Device Fingerprinting Use Case

TL;DR

This paper introduces a large-scale, comprehensive RF dataset for deep learning-based device fingerprinting, covering diverse environments, configurations, and hardware to facilitate research and validation of RF fingerprinting methods.

Contribution

It provides a publicly available, extensive RF dataset collected from 25 LoRa devices, including various experimental scenarios and configurations for advancing RF fingerprinting research.

Findings

Dataset enables robust deep learning model training.

Includes indoor and outdoor environment data.

Supports diverse device and configuration testing.

Abstract

Deep learning-based RF fingerprinting has recently been recognized as a potential solution for enabling newly emerging wireless network applications, such as spectrum access policy enforcement, automated network device authentication, and unauthorized network access monitoring and control. Real, comprehensive RF datasets are now needed more than ever to enable the study, assessment, and validation of newly developed RF fingerprinting approaches. In this paper, we present and release a large-scale RF fingerprinting dataset, collected from 25 different LoRa-enabled IoT transmitting devices using USRP B210 receivers. Our dataset consists of a large number of SigMF-compliant binary files representing the I/Q time-domain samples and their corresponding FFT-based files of LoRa transmissions. This dataset provides a comprehensive set of essential experimental scenarios, considering both indoor and outdoor environments and various network deployments and configurations, such as the distance between the transmitters and the receiver, the configuration of the considered LoRa modulation, the physical location of the conducted experiment, and the receiver hardware used for training and testing the neural network models.