用无线指纹可精准追踪超宽带设备，安全性存隐患
Tracking UWB Devices Through Radio Frequency Fingerprinting Is Possible
- 用深度学习从信号中提取硬件指纹特征
- 静态环境识别准确率超99%，动态环境仍达76%
- 适合研究物理层安全与隐私保护的学者
超宽带（UWB）技术因其厘米级定位能力被广泛应用于智能手机。其普及引发安全与隐私担忧。本文探索将射频指纹（RFF）技术应用于UWB的可行性，并研究其在不同环境下的泛化能力。我们使用市售UWB设备，在控制设备位置变化条件下采集真实数据集，并构建改进的深度学习管道以提取硬件签名。在稳定环境下，提取的RFF识别准确率超过99%；即便在未训练过的环境，准确率仍可达76%。结果表明，利用RFF对UWB设备进行物理层追踪是可行的。
原文摘要 · Abstract (English)
Ultra-wideband (UWB) is a state-of-the-art technology designed for applications requiring centimeter-level localization. Its widespread adoption by smartphone manufacturer naturally raises security and privacy concerns. Successfully implementing Radio Frequency Fingerprinting (RFF) to UWB could enable physical layer security, but might also allow undesired tracking of the devices. The scope of this paper is to explore the feasibility of applying RFF to UWB and investigates how well this technique generalizes across different environments. We collected a realistic dataset using off-the-shelf UWB devices with controlled variation in device positioning. Moreover, we developed an improved deep learning pipeline to extract the hardware signature from the signal data. In stable conditions, the extracted RFF achieves over 99% accuracy. While the accuracy decreases in more changing environments, we still obtain up to 76% accuracy in untrained locations.
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