Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks

TL;DR

This paper introduces a channel prediction-based physical layer authentication method using a Transformer model to effectively detect and prevent consecutive spoofing attacks in wireless networks, outperforming traditional approaches.

Contribution

It presents a novel Transformer-based channel prediction framework that enhances physical layer authentication robustness against sustained consecutive spoofing attacks.

Findings

Achieves low prediction error in Rayleigh fading channels

Significantly improves authentication accuracy over benchmarks

Maintains robustness during extended spoofing attacks

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.