Physical Tampering Detection Using Single COTS Wi-Fi Endpoint

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 (TPR) with no worse than a 4.12% false positive rate (FPR) in detecting physical tampering events.

Automated summary

This paper presents a practical physical tampering detection mechanism using inexpensive commercial off-the-shelf Wi-Fi endpoint devices and a deep neural network utilizing channel state information. The methodology is able to achieve a high true positive rate of 95.89% with a low false positive rate of 4.12% in detecting physical tampering events.