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帧间关系保护信号:一种用于射频指纹认证的新设计

Inter-Frame-Relationship Protected Signal: A New Design for Radio Frequency Fingerprint Authentication.

作者信息

Li Xufei, Zeng Shuiguang, Liu Yangyang

机构信息

School of Computer Science and Technology, Xidian University, Xi'an 710071, China.

College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China.

出版信息

Sensors (Basel). 2023 Aug 4;23(15):6948. doi: 10.3390/s23156948.

DOI:10.3390/s23156948
PMID:37571731
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10422603/
Abstract

Utilizing a multi-frame signal (MFS) rather than a single-frame signal (SFS) for radio frequency fingerprint authentication (RFFA) shows the advantage of higher accuracy. However, previous studies have often overlooked the associated security threats in MFS-based RFFA. In this paper, we focus on the carrier-sense multiple access with collision avoidance channel and identify a potential security threat, in that an attacker may inject a forged frame into valid traffic, making it more likely to be accepted alongside legitimate frames. To counter such a security threat, we propose an innovative design called the inter-frame-relationship protected signal (IfrPS), which enables the receiver to determine whether two consecutively received frames originate from the same transmitter to safeguard the MFS-based RFFA. To demonstrate the applicability of our proposition, we analyze and numerically evaluate two important properties: its impact on message demodulation and the accuracy gain in IfrPS-aided, MFS-based RFFA compared with the SFS-based RFFA. Our results show that the proposed scheme has a minimal impact of only -0.5 dB on message demodulation, while achieving up to 5 dB gain for RFFA accuracy.

摘要

利用多帧信号(MFS)而非单帧信号(SFS)进行射频指纹认证(RFFA)显示出更高准确性的优势。然而,先前的研究常常忽视基于MFS的RFFA中相关的安全威胁。在本文中,我们聚焦于带有冲突避免的载波侦听多路访问信道,并识别出一种潜在的安全威胁,即攻击者可能向有效流量中注入伪造帧,使其更有可能与合法帧一同被接受。为应对这种安全威胁,我们提出一种名为帧间关系保护信号(IfrPS)的创新设计,它能使接收器确定两个连续接收的帧是否源自同一发射机,以保障基于MFS的RFFA。为证明我们提议的适用性,我们分析并通过数值评估两个重要特性:它对消息解调的影响以及与基于SFS的RFFA相比,在基于IfrPS辅助的、基于MFS的RFFA中的准确性提升。我们的结果表明,所提出的方案对消息解调仅有-0.5 dB的最小影响,同时在RFFA准确性方面实现高达5 dB的增益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/c60904cb6acc/sensors-23-06948-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/5f07396bbca1/sensors-23-06948-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/0afc39a7199d/sensors-23-06948-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/bd793bba0ce6/sensors-23-06948-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/67dfc63430fb/sensors-23-06948-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/d52b5d20e0c2/sensors-23-06948-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/4360a32ca1e8/sensors-23-06948-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/cda3cef00060/sensors-23-06948-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/38b53abdb8ad/sensors-23-06948-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/f0dcdca1545f/sensors-23-06948-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/2cf62b423857/sensors-23-06948-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/72cccb5f2ca9/sensors-23-06948-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/9b9ae18daf6e/sensors-23-06948-g012a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/618fd71d0bac/sensors-23-06948-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/9664fc319ffe/sensors-23-06948-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/c60904cb6acc/sensors-23-06948-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/5f07396bbca1/sensors-23-06948-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/0afc39a7199d/sensors-23-06948-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/bd793bba0ce6/sensors-23-06948-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/67dfc63430fb/sensors-23-06948-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/d52b5d20e0c2/sensors-23-06948-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/4360a32ca1e8/sensors-23-06948-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/cda3cef00060/sensors-23-06948-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/38b53abdb8ad/sensors-23-06948-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/f0dcdca1545f/sensors-23-06948-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/2cf62b423857/sensors-23-06948-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/72cccb5f2ca9/sensors-23-06948-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/9b9ae18daf6e/sensors-23-06948-g012a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/618fd71d0bac/sensors-23-06948-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/9664fc319ffe/sensors-23-06948-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/caec/10422603/c60904cb6acc/sensors-23-06948-g015.jpg

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本文引用的文献

1
Deep-Learning-Based Physical Layer Authentication for Industrial Wireless Sensor Networks.用于工业无线传感器网络的基于深度学习的物理层认证
Sensors (Basel). 2019 May 28;19(11):2440. doi: 10.3390/s19112440.