Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices

2026-03-20

AI Summary

This work addresses the vulnerability of Internet-of-Things (IoT) devices to identity spoofing attacks stemming from the broadcast nature of wireless communications, a challenge exacerbated by the difficulty of existing physical-layer authentication methods in simultaneously achieving theoretical optimality and practical deployability. To bridge this gap, the authors propose LiteNP-Net, a lightweight neural network driven by a Neyman–Pearson (NP) detector. By embedding a conditional statistical model within a hypothesis testing framework, they derive a theoretically optimal detector and leverage its structure to guide network design, enabling near-optimal performance without requiring prior knowledge of channel statistics. Experimental results demonstrate that, in unknown channel conditions, LiteNP-Net approaches the NP bound in simulations and significantly outperforms both conventional correlation-based methods and state-of-the-art Siamese networks in real-world Wi-Fi scenarios.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The rise of wireless technologies has made the Internet of Things (IoT) ubiquitous, but the broadcast nature of wireless communications exposes IoT to authentication risks. Physical layer authentication (PLA) offers a promising solution by leveraging unique characteristics of wireless channels. As a common approach in PLA, hypothesis testing yields a theoretically optimal Neyman-Pearson (NP) detector, but its reliance on channel statistics limits its practicality in real-world scenarios. In contrast, deep learning-based PLA approaches are practical but tend to be not optimal. To address these challenges, we proposed a learning-based PLA scheme driven by hypothesis testing and conducted extensive simulations and experimental evaluations using Wi-Fi. Specifically, we incorporated conditional statistical models into the hypothesis testing framework to derive a theoretically optimal NP detector. Building on this, we developed LiteNP-Net, a lightweight neural network driven by the NP detector. Simulation results demonstrated that LiteNP-Net could approach the performance of the NP detector even without prior knowledge of the channel statistics. To further assess its effectiveness in practical environments, we deployed an experimental testbed using Wi-Fi IoT development kits in various real-world scenarios. Experimental results demonstrated that the LiteNP-Net outperformed the conventional correlation-based method as well as state-of-the-art Siamese-based methods.

Problem

• Physical Layer Authentication
• Wireless Security
• IoT Authentication
• Channel Statistics
• Model-Driven Learning

Innovation

• Physical Layer Authentication
• Neyman-Pearson Detector
• Model-Driven Learning
• LiteNP-Net
• Wi-Fi IoT