VeriPHY: Physical Layer Signal Authentication for Wireless Communication in 5G Environments

• URL: http://arxiv.org/abs/2508.09213v1
• Date: Mon, 11 Aug 2025 15:13:39 GMT
• Title: VeriPHY: Physical Layer Signal Authentication for Wireless Communication in 5G Environments
• Authors: Clifton Paul Robinson, Salvatore D'Oro, Tommaso Melodia,
• Abstract summary: Physical layer authentication (PLA) uses inherent characteristics of the communication medium to provide secure and efficient authentication in wireless networks.<n>With advancements in deep learning, PLA has become a widely adopted technique for its accuracy and reliability.<n>We introduce VeriPHY, a novel deep learning-based PLA solution for 5G networks.
• Score: 16.10137024446669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physical layer authentication (PLA) uses inherent characteristics of the communication medium to provide secure and efficient authentication in wireless networks, bypassing the need for traditional cryptographic methods. With advancements in deep learning, PLA has become a widely adopted technique for its accuracy and reliability. In this paper, we introduce VeriPHY, a novel deep learning-based PLA solution for 5G networks, which enables unique device identification by embedding signatures within wireless I/Q transmissions using steganography. VeriPHY continuously generates pseudo-random signatures by sampling from Gaussian Mixture Models whose distribution is carefully varied to ensure signature uniqueness and stealthiness over time, and then embeds the newly generated signatures over I/Q samples transmitted by users to the 5G gNB. Utilizing deep neural networks, VeriPHY identifies and authenticates users based on these embedded signatures. VeriPHY achieves high precision, identifying unique signatures between 93% and 100% with low false positive rates and an inference time of 28 ms when signatures are updated every 20 ms. Additionally, we also demonstrate a stealth generation mode where signatures are generated in a way that makes them virtually indistinguishable from unaltered 5G signals while maintaining over 93% detection accuracy.

Related papers

• One Signature, Multiple Payments: Demystifying and Detecting Signature Replay Vulnerabilities in Smart Contracts [56.94148977064169]
  lacking checks on signature usage conditions can lead to repeated verifications, increasing the risk of permission abuse and threatening contract assets.<n>We define this issue as the Signature Replay Vulnerability (SRV)<n>From 1,419 audit reports across 37 blockchain security companies, we identified 108 with detailed SRV descriptions and classified five types of SRVs.
  arXiv  Detail & Related papers  (2025-11-12T09:17:13Z)
• StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models [55.05404953041403]
  We propose a novel framework that seamlessly integrates a binary watermark into the diffusion generation process.<n>We show that StableGuard consistently outperforms state-of-the-art methods in image fidelity, watermark verification, and tampering localization.
  arXiv  Detail & Related papers  (2025-09-22T16:35:19Z)
• Watermarking and Anomaly Detection in Machine Learning Models for LORA RF Fingerprinting [0.0]
  We present a stronger RFFI system combining watermarking for ownership proof and anomaly detection for spotting suspicious inputs.<n>On the LoRa dataset, our system achieves 94.6% accuracy, 98% watermark success, and 0.94 AUROC, offering verifiable, tamper-resistant authentication.
  arXiv  Detail & Related papers  (2025-09-18T17:21:33Z)
• Privacy-Preserving Biometric Verification with Handwritten Random Digit String [49.77172854374479]
  Handwriting verification has stood as a steadfast identity authentication method for decades.<n>However, this technique risks potential privacy breaches due to the inclusion of personal information in handwritten biometrics such as signatures.<n>We propose using the Random Digit String (RDS) for privacy-preserving handwriting verification.
  arXiv  Detail & Related papers  (2025-03-17T03:47:25Z)
• Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis [57.7367843129838]
  Recent image generation schemes typically capture image distribution in a pre-constructed latent space relying on a frozen image tokenizer.<n>We propose a novel plug-and-play tokenizer training scheme to facilitate latent space construction.
  arXiv  Detail & Related papers  (2025-03-11T12:09:11Z)
• Block Induced Signature Generative Adversarial Network (BISGAN): Signature Spoofing Using GANs and Their Evaluation [1.0177118388531325]
  Generative adversarial networks (GANs) learn from original and forged signatures to generate forged signatures. This work focuses on creating a generator that produces forged samples that achieve a benchmark in spoofing signature verification systems.
  arXiv  Detail & Related papers  (2024-10-08T13:40:33Z)
• Consensus-Threshold Criterion for Offline Signature Verification using Convolutional Neural Network Learned Representations [0.0]
  A consensus-threshold distance-based classifier is proposed for offline writer-dependent signature verification. On GPDS-300, the consensus threshold classifier improves the state-of-the-art performance by achieving a 1.27% FAR compared to 8.73% and 17.31% recorded in literature. This is consistent across other datasets and guarantees that the risk of imposters gaining access to sensitive documents or transactions is minimal.
  arXiv  Detail & Related papers  (2024-01-05T23:10:26Z)
• SignCRF: Scalable Channel-agnostic Data-driven Radio Authentication System [17.391164797113134]
  Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique. The proposed SignCRF is a scalable, channel-agnostic, data-driven radio authentication platform. We demonstrate that SignCRF significantly improves the RFFDL performance by achieving as high as 5x and 8x improvement in correct authentication of WiFi and LoRa devices.
  arXiv  Detail & Related papers  (2023-03-21T21:11:02Z)
• Secure access system using signature verification over tablet PC [62.21072852729544]
  We describe a highly versatile and scalable prototype for Web-based secure access using signature verification. The proposed architecture can be easily extended to work with different kinds of sensors and large-scale databases.
  arXiv  Detail & Related papers  (2023-01-11T11:05:47Z)
• 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)
• Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming [97.40955121478716]
  We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations. We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
  arXiv  Detail & Related papers  (2020-10-22T12:32:29Z)
• Pre-print: Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM [1.293050392312921]
  It is estimated that the number of IoT devices will reach 75 billion in the next five years. Most of those currently, and to be deployed, lack sufficient security to protect themselves and their networks from attack by malicious IoT devices. This work presents a PHY layer IoT authentication approach capable of addressing this critical security need through the use of feature reduced Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM)
  arXiv  Detail & Related papers  (2020-05-19T15:02:20Z)

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.