Physical tampering detection using single cots wi?fi endpoint
Journal
Sensors
Journal Volume
21
Journal Issue
16
Date Issued
2021
Author(s)
Abstract
This paper proposes a practical physical tampering detection mechanism using inexpen-sive 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 rel-ative orientation between the Wi?Fi infrastructure and the endpoint, in contrast to previous sophis-ticated, 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. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Channel state information (CSI)
COTS Wi?Fi mobile device
Deep neural network (DNN)
Physical tampering detection
Single embedded antenna
Antennas
Commercial off-the-shelf
Deep neural networks
Embedded antenna
False positive rates
Subcarrier
Tampering detection
True positive rates
Channel state information
article
deep neural network
positivity rate
preliminary data
Neural Networks, Computer
Type
journal article