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The Day-After-Tomorrow: On the Performance of Radio Fingerprinting over Time

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submitted on 2024-08-13, 12:24 and posted on 2024-08-13, 12:25 authored by Alhazbi Saeif, Sciancalepore Savio, Oligeri Gabriele

The performance of Radio Frequency (RF) Fingerprinting (RFF) techniques is negatively impacted when the training data is not temporally close to the testing data. This can limit the practical implementation of physical-layer authentication solutions. To circumvent this problem, current solutions involve collecting training and testing datasets at close time intervals—this being detrimental to the real-life deployment of any physical-layer authentication solution. We refer to this issue as the Day-After-Tomorrow (DAT) effect, being widely attributed to the temporal variability of the wireless channel, which masks the physical-layer features of the transmitter, thus impairing the fingerprinting process. In this work, we investigate the DAT effect shedding light on its root causes. Our results refute previous knowledge by demonstrating that the DAT effect is not solely caused by the variability of the wireless channel. Instead, we prove that it is also due to the power cycling of the radios, i.e., the turning off and on of the radios between the collection of training and testing data. We show that state-of-the-art RFF solutions double their performance when the devices under test are not power cycled, i.e., the accuracy increases from about 0.5 to about 1 in a controlled scenario. Finally, we show how to mitigate the DAT effect in real-world scenarios, through pre-processing of the I-Q samples. Our experimental results show a significant improvement in accuracy, from approximately 0.45 to 0.85. Additionally, we reduce the variance of the results, making the overall performance more reliable.

Other Information

Published in: Annual Computer Security Applications Conference
License: https://creativecommons.org/licenses/by-nc/4.0/
See article on publisher's website: https://dx.doi.org/10.1145/3627106.3627192

Funding

Qatar National Research Fund (GSRA7-1-0510-20045), PhD-Machine Learning for Wireless Physical Layer Security.

Qatar National Research Fund (NPRP12C-0814-190012), Multi-layer Cybersecurity and Situational Awareness to Enhance Resiliency in Qatar’s Power Grid.

History

Language

  • English

Publisher

Association for Computing Machinery

Publication Year

  • 2023

License statement

This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

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