Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Taiwan University, Taipei 10617, Taiwan.
Sensors (Basel). 2021 Aug 23;21(16):5665. doi: 10.3390/s21165665.
This paper proposes a practical physical tampering detection mechanism using inexpensive commercial off-the-shelf (COTS) Wi-Fi endpoint devices with a deep neural network (DNN) on channel state information (CSI) in the Wi-Fi signals. Attributed to the DNN that identifies physical tampering events due to the multi-subcarrier characteristics in CSI, our methodology takes effect using only one COTS Wi-Fi endpoint with a single embedded antenna to detect changes in the relative orientation between the Wi-Fi infrastructure and the endpoint, in contrast to previous sophisticated, proprietary approaches. Preliminary results show that our detectors manage to achieve a 95.89% true positive rate () with no worse than a 4.12% false positive rate () in detecting physical tampering events.
本文提出了一种实用的物理篡改检测机制，使用价格低廉的商用现成（COTS）Wi-Fi 终端设备和 Wi-Fi 信号中的信道状态信息（CSI）上的深度神经网络（DNN）。由于 DNN 能够识别 CSI 中的多载波特性导致的物理篡改事件，我们的方法仅使用一个具有单个嵌入式天线的 COTS Wi-Fi 终端即可检测 Wi-Fi 基础设施和终端之间的相对方向变化，与以前复杂的专有方法形成对比。初步结果表明，我们的探测器在检测物理篡改事件时，能够实现 95.89%的真阳性率（TPR），假阳性率（FPR）不低于 4.12%。
