Preventing Radio Fingerprinting through Friendly Jamming

• URL: http://arxiv.org/abs/2407.08311v1
• Date: Thu, 11 Jul 2024 09:01:46 GMT
• Title: Preventing Radio Fingerprinting through Friendly Jamming
• Authors: Muhammad Irfan, Savio Sciancalepore, Gabriele Oligeri,
• Abstract summary: 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.
• Score: 5.074726108522963
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Radio Frequency fingerprinting enables a passive receiver to recognize and authenticate a transmitter without the need for cryptographic tools. Authentication is achieved by isolating specific features of the transmitted signal that are unique to the transmitter's hardware. Much research has focused on improving the effectiveness and efficiency of radio frequency fingerprinting to maximize its performance in various scenarios and conditions, while little research examined how to protect devices from being subject to radio fingerprinting in the wild. In this paper, we explore a novel point of view. We examine the hostile usage of radio frequency fingerprinting, which facilitates the unauthorized tracking of wireless devices in the field by malicious entities. We also suggest a method to sanitize the transmitted signal of its fingerprint using a jammer, deployed on purpose to improve devices' anonymity on the channel while still guaranteeing the link's quality of service. Our experimental results and subsequent analysis demonstrate that a friendly jammer can effectively block a malicious eavesdropper from recognizing and tracking a device without affecting the quality of the wireless link, thereby restoring the privacy of the user when accessing the radio spectrum.

Related papers

• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• 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)
• Cybersecurity Assessment of the Polar Bluetooth Low Energy Heart-rate Sensor [0.0]
  Bluetooth low energy (BLE) is a low-power protocol widely used in wireless personal area networks (WPANs) This paper analyzes the security vulnerabilities of a BLE heart-rate sensor. Case-study shows that an attacker can easily intercept and manipulate the data transmitted between the mobile app and the BLE device.
  arXiv  Detail & Related papers  (2024-04-24T18:18:09Z)
• 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)
• Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain [77.8858706250075]
  This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Our method performs very well with several classical face recognition test sets.
  arXiv  Detail & Related papers  (2022-07-15T07:15:36Z)
• 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)
• Audio-Visual Person-of-Interest DeepFake Detection [77.04789677645682]
  The aim of this work is to propose a deepfake detector that can cope with the wide variety of manipulation methods and scenarios encountered in the real world. We leverage a contrastive learning paradigm to learn the moving-face and audio segment embeddings that are most discriminative for each identity. Our method can detect both single-modality (audio-only, video-only) and multi-modality (audio-video) attacks, and is robust to low-quality or corrupted videos.
  arXiv  Detail & Related papers  (2022-04-06T20:51:40Z)
• Radio-Assisted Human Detection [61.738482870059805]
  We propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods. We extract the radio localization and identifer information from the radio signals to assist the human detection. Experiments on the simulative Microsoft COCO dataset and Caltech pedestrian datasets show that the mean average precision (mAP) and the miss rate can be improved with the aid of radio information.
  arXiv  Detail & Related papers  (2021-12-16T09:53:41Z)
• 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.