Erasing Radio Frequency Fingerprints via Active Adversarial Perturbation
- URL: http://arxiv.org/abs/2406.07349v2
- Date: Wed, 12 Jun 2024 12:10:25 GMT
- Title: Erasing Radio Frequency Fingerprints via Active Adversarial Perturbation
- Authors: Zhaoyi Lu, Wenchao Xu, Ming Tu, Xin Xie, Cunqing Hua, Nan Cheng,
- Abstract summary: 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.
- Score: 19.88283575742985
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Radio Frequency (RF) fingerprinting is to identify a wireless device from its uniqueness of the analog circuitry or hardware imperfections. However, unlike the MAC address which can be modified, such hardware feature is inevitable for the signal emitted to air, which can possibly reveal device whereabouts, e.g., a sniffer can use a pre-trained model to identify a nearby device when receiving its signal. Such fingerprint may expose critical private information, e.g., the associated upper-layer applications or the end-user. In this paper, we propose to erase such RF feature for wireless devices, which can prevent fingerprinting by actively perturbation from the signal perspective. Specifically, 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 perturbed pilot signal can hide the hardware feature and misclassify the model. We theoretically show that the perturbation would not affect the communication function within a tolerable perturbation threshold. We also implement the pilot signal fingerprinting and the proposed perturbation process in a practical LTE system. Extensive experiment results demonstrate that the RF fingerprints can be effectively erased to protect the user privacy.
Related papers
- 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) - RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge [66.33067693672696]
This paper addresses the critical problem of interference rejection in radio-frequency (RF) signals using a novel, data-driven approach.
First, we present an insightful signal model that serves as a foundation for developing and analyzing interference rejection algorithms.
Second, we introduce the RF Challenge, a publicly available dataset featuring diverse RF signals along with code templates.
Third, we propose novel AI-based rejection algorithms, specifically architectures like UNet and WaveNet, and evaluate their performance across eight different signal mixture types.
arXiv Detail & Related papers (2024-09-13T13:53:41Z) - Preventing Radio Fingerprinting through Friendly Jamming [5.074726108522963]
Radio frequency fingerprinting enables a passive receiver to recognize and authenticate a transmitter without the need for cryptographic tools.
We examine the hostile usage of radio frequency fingerprinting, which facilitates the unauthorized tracking of wireless devices in the field by malicious entities.
We suggest a method to sanitize the transmitted signal of its fingerprint using a jammer, deployed on purpose to improve devices' anonymity on the channel.
arXiv Detail & Related papers (2024-07-11T09:01:46Z) - Sticky Fingers: Resilience of Satellite Fingerprinting against Jamming Attacks [13.857226688708353]
We evaluate the effectiveness of radio fingerprinting techniques under interference and jamming attacks.
We conclude that it takes a similar amount of jamming power in order to disrupt the fingerprint as it does to jam the message contents itself.
arXiv Detail & Related papers (2024-02-07T17:28:09Z) - Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications.
Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems.
We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
arXiv Detail & Related papers (2023-12-08T13:50:30Z) - Task-Oriented Communications for NextG: End-to-End Deep Learning and AI
Security Aspects [78.84264189471936]
NextG communication systems are beginning to explore shifting this design paradigm to reliably executing a given task such as in task-oriented communications.
Wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label.
Task-oriented communications is considered by jointly training the transmitter, receiver and classifier functionalities as an encoder-decoder pair for the edge device and the gNodeB.
arXiv Detail & Related papers (2022-12-19T17:54:36Z) - 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) - CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an
In-Vehicle CAN Bus Based on Deep Features of Voltage Signals [48.813942331065206]
We propose a security hardening system for in-vehicle networks.
The proposed system includes two mechanisms that process deep features extracted from voltage signals measured on the CAN bus.
arXiv Detail & Related papers (2021-06-15T06:12:33Z) - Stay Connected, Leave no Trace: Enhancing Security and Privacy in WiFi
via Obfuscating Radiometric Fingerprints [8.89054576694426]
The intrinsic hardware imperfection of WiFi chipsets manifests itself in the transmitted signal, leading to a unique radiometric fingerprint.
Recent works propose practical fingerprinting solutions that can be readily implemented in commercial-off-the-shelf devices.
We show analytically and experimentally that these solutions are highly vulnerable to impersonation attacks.
We propose RF-Veil, a radiometric fingerprinting solution that not only is robust against impersonation attacks but also protects user privacy.
arXiv Detail & Related papers (2020-11-25T11:10:59Z) - Vision Meets Wireless Positioning: Effective Person Re-identification
with Recurrent Context Propagation [120.18969251405485]
Existing person re-identification methods rely on the visual sensor to capture the pedestrians.
Mobile phone can be sensed by WiFi and cellular networks in the form of a wireless positioning signal.
We propose a novel recurrent context propagation module that enables information to propagate between visual data and wireless positioning data.
arXiv Detail & Related papers (2020-08-10T14:19:15Z) - Device Authentication Codes based on RF Fingerprinting using Deep
Learning [2.980018103007841]
Device Authentication Code (DAC) is a novel method for authenticating IoT devices with wireless interface by exploiting their radio frequency (RF) signatures.
We show that DAC is able to prevent device impersonation by extracting salient features that are unique to any wireless device of interest.
arXiv Detail & Related papers (2020-04-19T01:50:29Z)
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.