Identification of Wearable Devices with Bluetooth

TL;DR

This paper presents a machine learning-based Bluetooth fingerprinting method to accurately identify wearable devices, enhancing security by detecting unauthorized or malicious wearables in IoT environments.

Contribution

It introduces a non-intrusive wearable fingerprinting framework utilizing 20 ML algorithms and demonstrates high accuracy in real-world tests with off-the-shelf smartwatches.

Findings

Achieved 98.5% average accuracy in device identification.

Demonstrated high precision and recall above 98%.

Validated effectiveness on real wearable devices.

Abstract

With wearable devices such as smartwatches on the rise in the consumer electronics market, securing these wearables is vital. However, the current security mechanisms only focus on validating the user not the device itself. Indeed, wearables can be (1) unauthorized wearable devices with correct credentials accessing valuable systems and networks, (2) passive insiders or outsider wearable devices, or (3) information-leaking wearables devices. Fingerprinting via machine learning can provide necessary cyber threat intelligence to address all these cyber attacks. In this work, we introduce a wearable fingerprinting technique focusing on Bluetooth classic protocol, which is a common protocol used by the wearables and other IoT devices. Specifically, we propose a non-intrusive wearable device identification framework which utilizes 20 different Machine Learning (ML) algorithms in the training phase of the classification process and selects the best performing algorithm for the testing phase. Furthermore, we evaluate the performance of proposed wearable fingerprinting technique on real wearable devices, including various off-the-shelf smartwatches. Our evaluation demonstrates the feasibility of the proposed technique to provide reliable cyber threat intelligence. Specifically, our detailed accuracy results show on average 98.5%, 98.3% precision and recall for identifying wearables using the Bluetooth classic protocol.