Real-time Bluetooth Low Energy vulnerability detection using TinyML on edge devices

Abdullaiev, Elmir та Sokolov, Volodymyr та Skladannyi, Pavlo та Kostiuk, Yuliia та Strelnikov, Vitalii (2026) Real-time Bluetooth Low Energy vulnerability detection using TinyML on edge devices Fifth International Workshop on Cyber Hygiene (4278). ISSN 1613-0073

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Анотація

Bluetooth Low Energy (BLE) has become ubiquitous in Internet of Things (IoT) ecosystems. However, this proliferation introduces significant security vulnerabilities, including device tracking, remote code execution, and privacy breaches. Existing detection methods either require expensive hardware, cloud connectivity, or lack real-time capability. We present a novel TinyML-based system that achieves real-time BLE vulnerability detection on ultra-low-cost edge devices. Our hybrid approach combines professional-grade data collection using Nordic nRF52840 with deployment on commodity ESP32-S3 microcontrollers. We collected and annotated 23,481 unique BLE advertisement packets from 13 diverse urban locations, implementing a rule-based labeling system with eight security criteria. Our deep neural network achieves 99.68% accuracy on test data and 97.98% in field validation (247 manually verified from 6,804 scanned devices), with a model size of only 4.62 KB and inference latency of 0.9 ms. Compared to state-of-the-art methods, our approach demonstrates 260 times smaller model size and 36 times faster inference while maintaining superior accuracy

Тип елементу :	Стаття
Ключові слова:	Bluetooth Low Energy; TinyML; edge AI; IoT security; vulnerability detection; neural network
Типологія:	Статті у базах даних > Scopus (без квартилю)
Підрозділи:	Факультет інформаційних технологій та математики > Кафедра інформаційної та кібернетичної безпеки ім. професора Володимира Бурячка
Користувач, що депонує:	Павло Миколайович Складанний
Дата внесення:	03 Лист 2026 22:06
Останні зміни:	03 Лист 2026 22:06
URI:	https://elibrary.kubg.edu.ua/id/eprint/60045

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