On the Impact of the Hardware Warm-Up Time on Deep Learning-Based RF Fingerprinting

TL;DR

This paper investigates how device hardware warm-up time affects deep learning RF fingerprinting accuracy, revealing significant performance drops during warm-up and providing datasets, explanations, and guidelines to improve robustness.

Contribution

It highlights the impact of hardware stabilization on RF fingerprinting, provides experimental evidence, releases a comprehensive dataset, and offers guidelines to enhance security robustness.

Findings

Accuracy drops below 37% when testing during warm-up

Accuracy exceeds 99% when both training and testing are post-stabilization

Warm-up time significantly affects RF fingerprinting performance

Abstract

Deep learning-based RF fingerprinting offers great potential for improving the security robustness of various emerging wireless networks. Although much progress has been done in enhancing fingerprinting methods, the impact of device hardware stabilization and warm-up time on the achievable fingerprinting performances has not received adequate attention. As such, this paper focuses on addressing this gap by investigating and shedding light on what could go wrong if the hardware stabilization aspects are overlooked. Specifically, our experimental results show that when the deep learning models are trained with data samples captured after the hardware stabilizes but tested with data captured right after powering on the devices, the device classification accuracy drops below 37%. However, when both the training and testing data are captured after the stabilization period, the achievable average accuracy exceeds 99%, when the model is trained and tested on the same day, and achieves 88% and 96% when the model is trained on one day but tested on another day, for the wireless and wired scenarios, respectively. Additionally, in this work, we leverage simulation and testbed experimentation to explain the cause behind the I/Q signal behavior observed during the device hardware warm-up time that led to the RF fingerprinting performance degradation. Furthermore, we release a large WiFi dataset, containing both unstable (collected during the warm-up period) and stable (collected after the warm-up period) captures across multiple days. Our work contributes datasets, explanations, and guidelines to enhance the robustness of RF fingerprinting in securing emerging wireless networks.