Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks

2026-03-20

AI Summary

This work addresses the severe performance degradation of conventional physical-layer authentication methods under continuous spoofing attacks, where time-varying channels hinder reliable identification of persistently forged packets. To overcome this limitation, the paper proposes a novel physical-layer authentication framework that integrates Transformer-based channel prediction with adaptive input updating. By forecasting legitimate channel state information (CSI) and dynamically refining prediction inputs based on real-time authentication outcomes, the framework effectively compensates for channel evolution caused by device mobility and fading. This approach uniquely combines channel prediction with a dynamic CSI update mechanism, achieving low prediction error in Rayleigh fading channels and significantly enhancing both authentication accuracy and robustness in continuous spoofing scenarios, outperforming existing traditional schemes.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Life-Long and Continual Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Authentication, authorization, and access control
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Wireless networks are highly vulnerable to spoofing attacks, especially when attackers transmit consecutive spoofing packets. Conventional physical layer authentication (PLA) methods have mostly focused on single-packet spoofing attack. However, under consecutive spoofing attacks, they become ineffective due to channel evolution caused by device mobility and channel fading. To address this challenge, we propose a channel prediction-based PLA framework. Specifically, a Transformer-based channel prediction module is employed to predict legitimate CSI measurements during spoofing interval, and the input of channel prediction module is adaptively updated with predicted or observed CSI measurements based on the authentication decision to ensure robustness against sustained spoofing. Simulation results under Rayleigh fading channels demonstrate that the proposed approach achieves low prediction error and significantly higher authentication accuracy than conventional benchmark, maintaining robustness even under extended spoofing attacks.

Problem

• spoofing attacks
• physical layer authentication
• channel prediction
• consecutive spoofing
• channel state information

Innovation

• channel prediction
• physical layer authentication
• consecutive spoofing attacks
• Transformer
• CSI