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Impact evaluation and detection of malicious spoofing attacks on BLE based occupancy detection systems

Impact evaluation and detection of malicious spoofing attacks on BLE based occupancy detection systems

Oliff, William, Filippoupolitis, Avgoustinos and Loukas, George (2017) Impact evaluation and detection of malicious spoofing attacks on BLE based occupancy detection systems. In: Proceedings of the International Conference on Internet of Things and Machine Learning (IML 2017). ACM. ISBN 978-1-4503-5243-7 (doi:https://doi.org/10.1145/3109761.3109776)

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Abstract

Occupancy detection is beneficial for applications such as emergency management and building energy management, as it provides information on the location of occupants. Internet of Things (IoT) devices such as Bluetooth Low Energy (BLE) beacons installed in a building can benefit the performance of occupancy detection systems, by providing information on an occupant's location. However, BLE beacons operate by broadcasting advertisement messages, and this renders them vulnerable to network attacks. Here, we evaluate the effect of two types of malicious spoofing attacks on a BLE based occupancy detection system, and propose an attack detection method. The building blocks of the system include BLE beacons installed inside the building, a mobile application installed on occupants' phones, and a remote control server where we perform occupancy detection using machine learning. Our real-world experimental results indicate that the attacks can significantly affect the system's performance. Also, our proposed detection method is able to accurately detect an attack by an adversary with physical access, with accuracy ranging from 84% to 91%.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the International Conference on Internet of Things and Machine Learning (IML 2017)
Additional Information: The International Conference on Internet of Things and Machine Learning (IML 2017), held from 17–18 October 2017, Liverpool John Moores University, Liverpool, UK.
Uncontrolled Keywords: iBeacon, Bluetooth Low Energy, Occupancy Detection, Machine Learning, Malicious Spoofing Attacks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Faculty of Architecture, Computing & Humanities > Internet of Things and Security (ISEC)
Related URLs:
Last Modified: 10 Jun 2019 12:06
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/17301

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