This paper presents a new radiometric fingerprint that is revealed by micro-signals in the channel state information (CSI) curves extracted from commodity Wi-Fi devices. We refer to this new fingerprint as "micro-CSI". Our experiments show that micro-CSI is likely to be caused by imperfections in the radio-frequency circuitry and is present in Wi-Fi 4/5/6 network interface cards (NICs). We conducted further experiments to determine the most effective CSI collection configuration to stabilize micro-CSI. To extract micro-CSI from varying CSI curves, we developed a signal space-based extraction algorithm that effectively separates distortions caused by wireless channels and hardware imperfections under line-of-sight (LoS) scenarios. Finally, we implemented a micro-CSI-based device authentication algorithm that uses the k-Nearest Neighbors (KNN) method to identify 11 COTS Wi-Fi NICs from the same manufacturer in typical indoor environments. Our experimental results demonstrate that the micro-CSI-based authentication algorithm can achieve an average attack detection rate of over 99% with a false alarm rate of 0%.

翻译：本文提出一种新的辐射测量指纹，该指纹通过从商用Wi-Fi设备提取的信道状态信息（CSI）曲线中的微信号揭示。我们将这种新指纹称为"微CSI"。实验表明，微CSI可能由射频电路中的缺陷引起，并存在于Wi-Fi 4/5/6网络接口卡（NIC）中。我们进一步开展实验，确定了最稳定的微CSI收集配置。为从变化的CSI曲线中提取微CSI，我们开发了一种基于信号空间的提取算法，该算法能有效分离视距（LoS）场景下由无线信道和硬件缺陷造成的失真。最后，我们实现了一种基于微CSI的设备认证算法，该算法使用k近邻（KNN）方法，在典型室内环境中识别来自同一制造商的11种商用Wi-Fi NIC。实验结果表明，基于微CSI的认证算法在误报率为0%时，平均攻击检测率可达99%以上。