| 引用本文： |
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王浚哲,徐锦龙,孟祥,魏冬,黄伟庆.基于人工极化指纹注入的物理层认证[J].信息安全学报,已采用 [点击复制]
- Wang Junzhe,Xu Jinlong,Meng Xiang,Wei Dong,Huang Weiqing.Physical Layer Authentication Based on Artificial Polarization Fingerprint Injection[J].Journal of Cyber Security,Accept [点击复制]
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| 摘要: |
| 物理层认证是6G内生安全的核心技术并受到越来越多的关注。射频指纹是一种前景广阔的物理层安全技术，具备实现低成本低功耗安全接入认证的潜力。然而由于制造技术的改进，导致相同型号设备射频指纹差异的减少，从而限制了识别系统的设备容量。为了解决这一问题，本文首次提出了基于人工极化指纹注入(artificial polarization fingerprint injection, APFI)物理层认证方案。该方案通过天线表面的便捷开槽实现，提高了指纹的差异性和识别系统的设备容量。APFI方案可以在物联网设备传输的信号中实现可配置的极化指纹注入。我们建立了表面电流密度分布模型，并分析了天线开槽对其极化带来的变化。我们发现表面开槽会产生频率相关性变化，利用这一特点进一步提升指纹差异。通过控制开槽参数，产生复杂且不同的极化模式色散现象，保证了注入指纹的唯一性。此外，我们还讨论了注入指纹的可靠性与天线性能之间的权衡，并为实际设计提供了指导。通过仿真，APFI方案可使注入的指纹数量成倍增长。大量结果表明，在99.5%的识别准确率下，APFI与原始指纹相比使得系统设备容量提高5倍。最后，通过基于实际贴片天线的大量仿真和实验评估了该解决方案的认证性能和鲁棒性，并分析了该解决方案应对穷举攻击和伪造攻击的能力。相比于现有方案，APFI的平均准确率分别超过原始极化指纹8.31%，射频指纹12.35%。 |
| 关键词: 物理层认证，电磁指纹，人工指纹注入 |
| DOI： |
| 投稿时间：2024-07-10修订日期：2024-10-17 |
| 基金项目:国家重点基础研究发展计划（973计划） |
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| Physical Layer Authentication Based on Artificial Polarization Fingerprint Injection |
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Wang Junzhe, Xu Jinlong, Meng Xiang, Wei Dong, Huang Weiqing
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| (institute of information engineering, cas) |
| Abstract: |
| Physical layer authentication is a core technology for intrinsic security in 6G and has gained increasing attention in the era of the Internet of Things. Radio Frequency Fingerprint (RFF) is a promising physical layer security technology with the potential to achieve low-cost, low-power, and secure access authentication. However, due to the manufacturing technology improvement, RFFs suffer from critical drawbacks, especially the reduction in fingerprint differences of devices with the same model, thereby limiting the device capacity of identification systems. To address this issue, this paper firstly presents a physical layer authentication scheme based on artificial polarization fingerprint injection (APFI). Our scheme is implemented through convenient slots on the surface of antenna, which improves fingerprint differentiation and increases the device capacity of the identification system. And the APFI scheme can realize configurable polarization fingerprint injection in signals transmitted by IoT devices. We establish a surface current density distribution model and analyze the polarization changes induced by antenna slotting. Our findings show that surface slotting produces frequency-dependent changes, which are exploited to further enhance fingerprint disparity. By controlling slotting parameters, complex and different polarization mode dispersion effects are caused, ensuring the reliability of injected fingerprints. Additionally, we discuss the trade-off between the reliability of injected fingerprints and antenna performance, providing guidance for practical design. According to our simulations, the APFI scheme can multiply the number of injected fingerprints. Extensive results show that APFI improves the device capacity of identification system by more than 5 times compared with the original polarization fingerprints at 99.5% identification accuracy. Finally, extensive simulations and experiments based on actual patch antennas evaluate the authentication performance and robustness of the solution, and its ability to counter exhaustive attack and forgery attack is analyzed. Compared to the state-of-the-art, the average accuracy of APFI exceeds that of original polarization fingerprints by 8.31% and RFFs by 12.35%, respectively. |
| Key words: physical layer authentication electromagnetic fingerprint artificially injected fingerprints |
