Physical Tampering Detection Using Single COTS Wi-Fi Endpoint

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 16; p. 5665
• Main Authors: Chan, Poh Yuen, Lai, Alexander I-Chi, Wu, Pei-Yuan, Wu, Ruey-Beei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 23.08.2021; MDPI
• Subjects: Accuracy; Antennas; Bandwidths; channel state information (CSI); Communication; COTS Wi-Fi mobile device; deep neural network (DNN); Experiments; Internet of Things; Localization; Neural networks; physical tampering detection; Receivers & amplifiers; Sensors; single embedded antenna; Software; Surveillance; Transmitters; Virtual private networks
• ISSN: 1424-8220; 1424-8220
• 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 (TPR) with no worse than a 4.12% false positive rate (FPR) in detecting physical tampering events.