Abstract
Smartphones have become essential components in the Internet of Medical Things (IoMT), providing convenient interfaces and advanced technology that enable interaction with various medical devices and sensors. This makes smartphones serve as gateways for sensitive data that could potentially affect patients’ health and privacy if compromised, making them primary targets for cybersecurity threats. Authentication is crucial for IoMT security, as its effectiveness relies on its resistance to any conditions of environment, device, or user. In this paper, we propose the Anomaly Location-based Authentication (ALBA) method using GPS technology and a lightweight unsupervised ML algorithm with more stable features. Our experimental results showed that the model successfully identified anomalous locations across three distinct datasets, demonstrating the adaptability of ALBA.
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Alruwaili, F.J., Mohanty, S.P., Kougianos, E. (2024). ALBA: Novel Anomaly Location-Based Authentication in IoMT Environment Using Unsupervised ML. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-031-45878-1_30
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DOI: https://doi.org/10.1007/978-3-031-45878-1_30
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