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Tsinghua Science and Technology 2023, 28(5): 862-872 https://doi.org/10.26599/TST.2022.9010059
Open Access | Issue | Published: 19 May 2023

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

Show Author's Information Jinghui Zhang1,  Xinyang Li1,  Junhe Li2,  Qiangsheng Dai3,  Zhen Ling1,  Ming Yang1( )
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
Keywords:
link layer fingerprint, software-defined radio, Bluetooth Low Energy (BLE)
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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Abstract Full text About this article

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.


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About this article

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

Show Author's information Jinghui Zhang1, Xinyang Li1, Junhe Li2, Qiangsheng Dai3, Zhen Ling1, Ming Yang1( )
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

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 fingerprint, software-defined radio, Bluetooth Low Energy (BLE)

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Publication history

Received: 24 November 2022
Accepted: 10 December 2022
Published: 19 May 2023
Issue date: October 2023

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© The author(s) 2023.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61972085, 62072103, and 62232004), the Jiangsu Provincial Key R&D Program (Nos. BE2021729, BE2022680, and BE2022065-4), the Jiangsu Provincial Key Laboratory of Network and Information Security (No. BM2003201), the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China (No. 93K-9), the Collaborative Innovation Center of Novel Software Technology and Industrialization, the Fundamental Research Funds for the Central Universities, the CCF-Baidu Open Fund (No. 2021PP15002000), and the Future Network Scientific Research Fund Project (No. FNSRFP-2021-YB-02).

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