Manchester Metropolitan University's Research Repository

    Secure Bluetooth communication in smart healthcare systems: a novel community dataset and intrusion detection system

    Zubair, Mohammed ORCID logoORCID: https://orcid.org/0000-0002-1844-2080, Ghubaish, Ali ORCID logoORCID: https://orcid.org/0000-0003-3358-7680, Unal, Devrim ORCID logoORCID: https://orcid.org/0000-0003-3146-3502, Al-Ali, Abdulla ORCID logoORCID: https://orcid.org/0000-0002-3527-2554, Reimann, Thomas ORCID logoORCID: https://orcid.org/0000-0002-8878-8909, Alinier, Guillaume ORCID logoORCID: https://orcid.org/0000-0003-4255-4450, Hammoudeh, Mohammad ORCID logoORCID: https://orcid.org/0000-0003-1058-0996 and Qadir, Junaid ORCID logoORCID: https://orcid.org/0000-0001-9466-2475 (2022) Secure Bluetooth communication in smart healthcare systems: a novel community dataset and intrusion detection system. Sensors, 22 (21). 8280. ISSN 1424-8220

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    Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores.

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