Authors:
Achyuth Nandikotkur
1
;
Issa Traore
1
and
Mohammad Mamun
2
Affiliations:
1
ECE Department, University of Victoria, British Columbia, Canada
;
2
National Research Council Canada, New Brunswick, Canada
Keyword(s):
Bluetooth Security, Intrusion Detection, Machine Learning, BrakTooth.
Abstract:
More than 5.1 billion Bluetooth-enabled devices were shipped in the year 2022 and this trend is expected to exceed 7.1 billion by the year 2026. A large proportion of these devices are used in smart homes designed for older adults, to help them age in place. Monitoring vitals, climate control, illumination control, fall detection, incontinence detection, pill dispensing, and several other functions are successfully addressed by many of these Bluetooth-enabled devices. Therefore it becomes crucial to protect them from malicious attacks and ensure the safety and well-being of their users. Some of these devices have only Bluetooth connectivity which makes patching them challenging for older adults, as a result, most remain unpatched. The family of vulnerabilities recently found in the Bluetooth Classic (BT Classic) stack called BrakTooth, poses a genuine threat to such devices. In this study, we develop an experimental procedure to capture traffic at the Link Manager Protocol (LMP) laye
r of the BT Classic stack and use machine learning algorithms to detect BrakTooth-based attacks.
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