Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible
- URL: http://arxiv.org/abs/2501.04401v1
- Date: Wed, 08 Jan 2025 10:29:35 GMT
- Title: Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible
- Authors: Thibaud Ardoin, Niklas Pauli, Benedikt Groß, Mahsa Kholghi, Khan Reaz, Gerhard Wunder,
- Abstract summary: Radio Frequency Fingerprinting (RFF) to Ultra-wideband (UWB) could enable physical layer security, but might also allow undesired tracking of the devices.<n>We collected a realistic dataset using off-the-shelf UWB devices with controlled variation in device positioning.<n>In stable conditions, the extracted RFF achieves over 99% accuracy. While the accuracy decreases in more changing environments, we still obtain up to 76% accuracy in untrained locations.
- Score: 1.2138840417631502
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ultra-wideband (UWB) is a state-of-the-art technology designed for applications requiring centimeter-level localization. Its widespread adoption by smartphone manufacturer naturally raises security and privacy concerns. Successfully implementing Radio Frequency Fingerprinting (RFF) to UWB could enable physical layer security, but might also allow undesired tracking of the devices. The scope of this paper is to explore the feasibility of applying RFF to UWB and investigates how well this technique generalizes across different environments. We collected a realistic dataset using off-the-shelf UWB devices with controlled variation in device positioning. Moreover, we developed an improved deep learning pipeline to extract the hardware signature from the signal data. In stable conditions, the extracted RFF achieves over 99% accuracy. While the accuracy decreases in more changing environments, we still obtain up to 76% accuracy in untrained locations.
Related papers
- Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model [6.566363978473944]
Radio Frequency Fingerprint Identification (RFFI) is a promising authentication technique to identify wireless devices.
RFFI performance under low signal-to-noise ratio (SNR) scenarios is significantly degraded because the minute hardware features can be easily swamped in noise.
We leveraged the diffusion model to effectively restore the RFF under low SNR scenarios.
arXiv Detail & Related papers (2025-03-07T15:30:55Z) - DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users [52.9870460238443]
We propose a deep reinforcement learning-based blind beamforming technique using a learnable Dolph-Tschebyscheff antenna array.
Our simulation results show that the proposed method can support data rates very close to the best possible values.
arXiv Detail & Related papers (2025-02-03T11:50:43Z) - HidePrint: Hiding the Radio Fingerprint via Random Noise [3.9901365062418312]
HidePrint hides the transmitter's fingerprint against an illegitimate eavesdropper by injecting controlled noise in the transmitted signal.
We introduce selective radio fingerprint disclosure, a new technique that allows the transmitter to disclose the radio fingerprint to only a subset of intended receivers.
arXiv Detail & Related papers (2024-11-10T10:45:35Z) - Advancements in UWB: Paving the Way for Sovereign Data Networks in Healthcare Facilities [5.3044349060590905]
We argue that UWB data communication holds significant potential in healthcare and ultra-secure environments.
A sovereign UWB network could serve as an alternative, providing secure localization and short-range data communication in such environments.
arXiv Detail & Related papers (2024-08-08T12:43:47Z) - Erasing Radio Frequency Fingerprints via Active Adversarial Perturbation [19.88283575742985]
We consider a common RF fingerprinting scenario, where machine learning models are trained from pilot signal data for identification.
A novel adversarial attack solution is designed to generate proper perturbations, whereby the pilot signal can hide the hardware feature and misclassify the model.
Extensive experiment results demonstrate that the RF fingerprints can be effectively erased to protect the user privacy.
arXiv Detail & Related papers (2024-06-11T15:16:05Z) - Frequency-Aware Deepfake Detection: Improving Generalizability through
Frequency Space Learning [81.98675881423131]
This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images.
Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries.
We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
arXiv Detail & Related papers (2024-03-12T01:28:00Z) - Disentangled Representation Learning for RF Fingerprint Extraction under
Unknown Channel Statistics [77.13542705329328]
We propose a framework of disentangled representation learning(DRL) that first learns to factor the input signals into a device-relevant component and a device-irrelevant component via adversarial learning.
The implicit data augmentation in the proposed framework imposes a regularization on the RFF extractor to avoid the possible overfitting of device-irrelevant channel statistics.
Experiments validate that the proposed approach, referred to as DR-RFF, outperforms conventional methods in terms of generalizability to unknown complicated propagation environments.
arXiv Detail & Related papers (2022-08-04T15:46:48Z) - Decision Forest Based EMG Signal Classification with Low Volume Dataset
Augmented with Random Variance Gaussian Noise [51.76329821186873]
We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience.
We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
arXiv Detail & Related papers (2022-06-29T23:22:18Z) - GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action
Recognition using WiFi [52.530330427538885]
WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring.
We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
arXiv Detail & Related papers (2022-05-24T10:20:16Z) - LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning [59.17191114000146]
LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs)
In the proposed method, the user to be localized reports the RSS from BSs to a Central Processing Unit ( CPU) which may be located in the cloud.
Using estimated pathloss radio maps of the BSs, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the radio maps.
arXiv Detail & Related papers (2022-02-01T20:27:46Z) - Heading Estimation Using Ultra-Wideband Received Signal Strength and
Gaussian Processes [1.6058099298620425]
This letter experimentally demonstrates how to use UWB range and received signal strength ( RSS) measurements to estimate robot heading.
A gyroscope in an invariant extended Kalman filter is used to realize a heading estimation method that uses only UWB and gyroscope measurements.
arXiv Detail & Related papers (2021-09-10T13:28:23Z) - Real-time Outdoor Localization Using Radio Maps: A Deep Learning
Approach [59.17191114000146]
LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task.
We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
arXiv Detail & Related papers (2021-06-23T17:27:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.