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Open Access

Bluetooth Low Energy Device Identification Based on Link Layer Broadcast Packet Fingerprinting

School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
Research Institute, State Grid Jiangsu Electric Power Company Ltd., Nanjing 210024, China
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Abstract

With the rapid development of the Internet of Things (IoT), wireless technology has become an indispensable part of modern computing platforms and embedded systems. Wireless device fingerprint identification is deemed as a promising solution towards enhancing the security of device access authentication and communication process in the IoT scenario. However, the extraction of features from the network layer and its upper layers often confront restrictions from specific devices: the association with a certain wireless network and the access to the plaintext of the payload. Meanwhile, Bluetooth Low Energy (BLE) packets have been encrypted above the link layer, which makes those features difficult to extract. To tackle these problems, we introduce a novel method to identify BLE devices based on the fingerprint features in the data link layer. Initially, the BLE packets are collected through a receiver based on software-defined radio technology. Then, fields that reflect device differences in BLE broadcast packets are extracted through traffic analysis. Finally, a MultiLayer Perceptron (MLP) model is employed to recognize the category of BLE devices. An experimental result on a dataset with 15 types of BLE devices shows that the identification accuracy of the proposed method can reach 99.8%, which accomplishes better performance over previous work.

Keywords

link layer fingerprintsoftware-defined radioBluetooth Low Energy (BLE)

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Tsinghua Science and Technology
Volume 28 Issue 5,
October 2023
Pages 862-872
DOI: 10.26599/TST.2022.9010059
Cite this article:
Zhang J, Li X, Li J, et al. Bluetooth Low Energy Device Identification Based on Link Layer Broadcast Packet Fingerprinting. Tsinghua Science and Technology, 2023, 28(5): 862-872. https://doi.org/10.26599/TST.2022.9010059

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Received: 24 November 2022
Accepted: 10 December 2022
Published: 19 May 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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