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arXiv 2603.19962cs.CRcs.LG
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基于通道预测的物理层认证在连续伪装攻击下的应用

Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks

School of Computer Science and Informatics, University of Liverpool(利物浦大学计算机科学与信息学学院);Institute of Communications Engineering, National Tsing Hua University(国立清华大学通讯工程研究所)

Yijia Guo, Junqing Zhang, Yao-Win Peter Hong

AI总结:

本文提出基于通道预测的物理层认证框架,用于应对连续伪装攻击。通过Transformer模块预测合法CSI测量,结合认证决策动态更新输入,提升对抗持续伪装的鲁棒性。

摘要:

无线网络极易受到伪装攻击的威胁,尤其是在攻击者连续发送伪装数据包时。传统物理层认证方法主要针对单包伪装攻击,但在连续伪装攻击下,由于设备移动和信道衰减导致的信道演变,它们变得无效。为了解决这一挑战,我们提出了一种基于通道预测的物理层认证框架。具体而言,采用基于Transformer的通道预测模块来预测伪装间隔内的合法CSI测量,并根据认证决策动态更新通道预测模块的输入,以确保对持续伪装的鲁棒性。在瑞利衰落信道下的仿真结果表明,所提出的方法在预测误差低且认证准确率显著高于传统基准,即使在扩展的伪装攻击下也能保持鲁棒性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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