Alsemmeari, R.A.; Dahab, M.Y.; Alsulami, A.A.; Alturki, B.; Algarni, S. Resilient Security Framework Using TNN and Blockchain for IoMT. Electronics2023, 12, 2252.
Alsemmeari, R.A.; Dahab, M.Y.; Alsulami, A.A.; Alturki, B.; Algarni, S. Resilient Security Framework Using TNN and Blockchain for IoMT. Electronics 2023, 12, 2252.
Alsemmeari, R.A.; Dahab, M.Y.; Alsulami, A.A.; Alturki, B.; Algarni, S. Resilient Security Framework Using TNN and Blockchain for IoMT. Electronics2023, 12, 2252.
Alsemmeari, R.A.; Dahab, M.Y.; Alsulami, A.A.; Alturki, B.; Algarni, S. Resilient Security Framework Using TNN and Blockchain for IoMT. Electronics 2023, 12, 2252.
Abstract
The growth in the Internet of Things (IoT) devices in the healthcare sector enables the new era of the Internet of Medical Things (IoMT). However, IoT devices are susceptible to diverse cybersecurity attacks and threats that lead to negative consequences. Cyberattacks can harm not just the IoMT devices being used but also human life. Currently, several security solutions are proposed to enhance the security of the IoMT, which uses machine learning (ML) and blockchain. ML can be used to develop detection and classification methods to identify cyberattacks targeting IoMT devices in the healthcare sector. In addition, blockchain technology enables a decentralized approach to the healthcare system and eliminates some disadvantages of a centralized system, such as a single point of failure. This paper proposes a resilient security framework integrating a Tri-layered Neural Network (TNN) and blockchain technology in the healthcare domain. The TNN detects anomalies in data measured by medical sensors to find fraudulent data. Therefore, cyberattacks are detected and discarded from the IoMT system before data is processed at the fog layer. In addition, a blockchain network is used in the fog layer to ensure that the data is not altered, boosting the integrity and privacy of the medical data. The experimental results show that the TNN and blockchain models produce the expected result. Furthermore, the accuracy of the TNN model reached 99.99% on the F1-score accuracy metric.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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