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author:

Cai, Hanlin (Cai, Hanlin.) [1] | Fang, Yuchen (Fang, Yuchen.) [2] | Huang, Jiacheng (Huang, Jiacheng.) [3] | Yuan, Meng (Yuan, Meng.) [4] | Xu, Zhezhuang (Xu, Zhezhuang.) [5]

Indexed by:

EI

Abstract:

As the foremost protocol for low-power communication, Bluetooth Low Energy (BLE) significantly impacts various aspects of our lives, including industry and healthcare. Given BLE's inherent security limitations and firmware vulnerabilities, spoofing attacks can readily compromise BLE devices and jeopardize privacy data. In this paper, we introduce BLEGuard, a hybrid mechanism for detecting spoofing attacks in BLE networks. We established a physical Bluetooth system to conduct attack simulations and construct a substantial dataset (BLE-SAD). BLEGuard integrates pre-detection, reconstruction, and classification models to effectively identify spoofing activities, achieving an impressive preliminary accuracy of 99.01%, with a false alarm rate of 2.05% and an undetection rate of 0.36%. © 2024 Copyright is held by the owner/author(s).

Keyword:

Bluetooth Deep learning Firmware Low power electronics

Community:

  • [ 1 ] [Cai, Hanlin]National University of Ireland, Maynooth, Kildare, Ireland
  • [ 2 ] [Cai, Hanlin]Fuzhou University, Fuzhou, China
  • [ 3 ] [Fang, Yuchen]National University of Ireland, Maynooth, Kildare, Ireland
  • [ 4 ] [Huang, Jiacheng]National University of Ireland, Maynooth, Kildare, Ireland
  • [ 5 ] [Yuan, Meng]Chalmers University of Technology, Göteborg, Sweden
  • [ 6 ] [Yuan, Meng]Fuzhou University, Fuzhou, China
  • [ 7 ] [Xu, Zhezhuang]Fuzhou University, Fuzhou, China

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Year: 2024

Page: 710-711

Language: English

Cited Count:

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ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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