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Physical Tampering Detection Using Single COTS Wi-Fi Endpoint.

Poh Yuen Chan¹, Alexander I-Chi Lai¹, Pei-Yuan Wu¹

¹Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Taiwan University, Taipei 10617, Taiwan.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

This summary is machine-generated.

This study introduces a novel physical tampering detection system using Wi-Fi signals and deep neural networks (DNNs). The method effectively detects device tampering by analyzing channel state information (CSI) with high accuracy.

Keywords:

COTS Wi-Fi mobile device channel state information (CSI)deep neural network (DNN)physical tampering detection single embedded antenna

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Area of Science:

Cybersecurity
Wireless Networking
Machine Learning

Background:

Physical tampering poses a significant threat to the security of networked devices.
Existing detection methods are often complex, proprietary, and require multiple devices.

Purpose of the Study:

To develop a practical and cost-effective physical tampering detection mechanism.
To leverage readily available commercial off-the-shelf (COTS) Wi-Fi devices for security applications.

Main Methods:

Utilizing channel state information (CSI) from Wi-Fi signals.
Employing a deep neural network (DNN) to analyze multi-subcarrier characteristics in CSI.
Detecting changes in the relative orientation between Wi-Fi infrastructure and endpoints using a single COTS device.

Main Results:

Achieved a 95.89% true positive rate (TPR) in detecting physical tampering events.
Maintained a false positive rate (FPR) no worse than 4.12%.

Conclusions:

The proposed DNN-based approach offers an effective and accessible solution for physical tampering detection.
This method demonstrates the potential of COTS Wi-Fi devices for enhancing device security.