Securing UAV Communications by Fusing Cross-Layer Fingerprints

2025-11-08

IEEE Internet of Things Journal

AI Summary

To address spoofing attacks against UAVs in open wireless environments and the poor robustness of conventional single-modal fingerprinting authentication, this paper proposes SecureLink—the first cross-layer multimodal authentication framework integrating physical-layer RF fingerprints with application-layer MEMS sensor fingerprints. SecureLink jointly models channel state information (CSI) and flight dynamics, leveraging attention-driven feature fusion, multi-similarity loss optimization, and one-class support vector machines to achieve high-accuracy identity verification under open-world conditions. Extensive experiments on three real-world UAV platforms demonstrate that SecureLink achieves 98.61% closed-set accuracy and 97.54% open-set accuracy using only six additional data frames—significantly outperforming state-of-the-art methods. The framework substantially enhances authentication reliability and adaptability in mobile, open wireless scenarios.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Securing UAV communications against wireless impersonation attacks
• Overcoming unreliable single-layer authentication in mobile environments
• Fusing cross-layer fingerprints to enhance UAV authentication reliability

Innovation

• Fuses physical and application layer fingerprints
• Uses attention-based neural network for feature fusion
• Employs one-class SVM for open-world authentication