Securing UAV Communications by Fusing Cross-Layer Fingerprints

• URL: http://arxiv.org/abs/2511.05796v1
• Date: Sat, 08 Nov 2025 02:00:31 GMT
• Title: Securing UAV Communications by Fusing Cross-Layer Fingerprints
• Authors: Yong Huang, Ruihao Li, Mingyang Chen, Feiyang Zhao, Dalong Zhang, Wanqing Tu,
• Abstract summary: Open nature of wireless communications renders unmanned aerial vehicle (UAV) communications vulnerable to impersonation attacks.<n>Traditional fingerprint-based UAV authentication approaches rely on a single modality of sensory data gathered from a single layer of the network model.<n>This paper proposes SecureLink, a UAV authentication system that is among the first to employ cross-layer information for enhancing the efficiency and reliability of UAV authentication.
• Score: 10.001607132877021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The open nature of wireless communications renders unmanned aerial vehicle (UAV) communications vulnerable to impersonation attacks, under which malicious UAVs can impersonate authorized ones with stolen digital certificates. Traditional fingerprint-based UAV authentication approaches rely on a single modality of sensory data gathered from a single layer of the network model, resulting in unreliable authentication experiences, particularly when UAVs are mobile and in an open-world environment. To transcend these limitations, this paper proposes SecureLink, a UAV authentication system that is among the first to employ cross-layer information for enhancing the efficiency and reliability of UAV authentication. Instead of using single modalities, SecureLink fuses physical-layer radio frequency (RF) fingerprints and application-layer micro-electromechanical system (MEMS) fingerprints into reliable UAV identifiers via multimodal fusion. SecureLink first aligns fingerprints from channel state information measurements and telemetry data, such as feedback readings of onboard accelerometers, gyroscopes, and barometers. Then, an attention-based neural network is devised for in-depth feature fusion. Next, the fused features are trained by a multi-similarity loss and fed into a one-class support vector machine for open-world authentication. We extensively implement our SecureLink using three different types of UAVs and evaluate it in different environments. With only six additional data frames, SecureLink achieves a closed-world accuracy of 98.61% and an open-world accuracy of 97.54% with two impersonating UAVs, outperforming the existing approaches in authentication robustness and communication overheads. Finally, our datasets collected from these experiments are available on GitHub: https://github.com/PhyGroup/SecureLink\_data.

Related papers

• A Tri-Modal Dataset and a Baseline System for Tracking Unmanned Aerial Vehicles [74.8162337823142]
  MM-UAV is the first large-scale benchmark for Multi-Modal UAV Tracking.<n>The dataset spans over 30 challenging scenarios, with 1,321 synchronised multi-modal sequences, and more than 2.8 million annotated frames.<n>Accompanying the dataset, we provide a novel multi-modal multi-UAV tracking framework.
  arXiv  Detail & Related papers  (2025-11-23T08:42:17Z)
• When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks [68.45202147860537]
  Low-altitude wireless networks (LAWNs) provide enhanced coverage, reliability, and throughput for diverse applications.<n>These networks face significant security vulnerabilities from both known and potential unknown eavesdroppers.<n>We propose a novel secure communication framework for LAWNs where the selected UAVs within a swarm function as a virtual antenna array.
  arXiv  Detail & Related papers  (2025-10-25T02:02:14Z)
• Towards Reliable Service Provisioning for Dynamic UAV Clusters in Low-Altitude Economy Networks [48.73244147035607]
  Unmanned Aerial Vehicle (UAV) cluster services are crucial for promoting the low-altitude economy by enabling scalable, flexible, and adaptive aerial networks.<n>We propose a Lightweight and Privacy-Preserving Cluster Authentication and Session Key Update (LP2-CA) scheme for dynamic UAV clusters in low-altitude economy networks.
  arXiv  Detail & Related papers  (2025-09-07T15:54:11Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Vehicular Communication Security: Multi-Channel and Multi-Factor Authentication [8.5073971104553]
  Vehicle-to-Infrastructure (V2I) communication plays a key role in enabling mobility-enhancing and safety-critical services.<n>Current V2I authentication relies on credential-based methods over wireless Non-Line-of-Sight (NLOS) channels.<n>We propose a unified Multi-Channel, Multi-Factor Authentication scheme that combines NLOS cryptographic credentials with a Line-of-Sight (LOS) visual channel.
  arXiv  Detail & Related papers  (2025-05-01T06:36:24Z)
• Securing UAV Communication: Authentication and Integrity [0.0]
  We propose an authentication method to secure UAV data exchange over an insecure communication channel. Our solution combines Diffie-Hellman key exchange and Hash-based Message Authentication Code (HMAC) within ROS communication channels. Both drones successfully detected tampered keys, affirming our method's efficacy in protecting UAV communication.
  arXiv  Detail & Related papers  (2024-10-06T22:36:06Z)
• BAZAM: A Blockchain-Assisted Zero-Trust Authentication in Multi-UAV Wireless Networks [21.51085709522321]
  Unmanned aerial vehicles (UAVs) are vulnerable to interception and attacks when operated remotely without a unified identity authentication. We introduce a blockchain-assisted zero-trust authentication scheme, namely BAZAM, designed for multi-UAV wireless networks.
  arXiv  Detail & Related papers  (2024-06-30T09:06:49Z)
• Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV Networking [21.471242542278233]
  Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. We propose a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management.
  arXiv  Detail & Related papers  (2023-11-15T02:17:59Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management [45.15174235000158]
  Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, artificial intelligence (AI) model training, and wireless communications. It is impractical to send raw data of devices to UAV servers for model training. In this paper, we develop an asynchronous federated learning framework for multi-UAV-enabled networks.
  arXiv  Detail & Related papers  (2020-11-28T18:58:34Z)
• 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)

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.