A Novel Zero-Trust Machine Learning Green Architecture for Healthcare IoT Cybersecurity: Review, Analysis, and Implementation

• URL: http://arxiv.org/abs/2401.07368v1
• Date: Sun, 14 Jan 2024 21:01:21 GMT
• Title: A Novel Zero-Trust Machine Learning Green Architecture for Healthcare IoT Cybersecurity: Review, Analysis, and Implementation
• Authors: Zag ElSayed, Nelly Elsayed, Sajjad Bay,
• Abstract summary: The integration of Internet of Things (IoT) devices in healthcare applications has revolutionized patient care, monitoring, and data management. However, the rapid involvement of these devices brings information security concerns that pose critical threats to patient privacy and the integrity of healthcare data. This paper introduces a novel machine learning (ML) based architecture explicitly designed to address and mitigate security vulnerabilities in IoT devices within healthcare applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of Internet of Things (IoT) devices in healthcare applications has revolutionized patient care, monitoring, and data management. The Global IoT in Healthcare Market value is $252.2 Billion in 2023. However, the rapid involvement of these devices brings information security concerns that pose critical threats to patient privacy and the integrity of healthcare data. This paper introduces a novel machine learning (ML) based architecture explicitly designed to address and mitigate security vulnerabilities in IoT devices within healthcare applications. By leveraging advanced convolution ML architecture, the proposed architecture aims to proactively monitor and detect potential threats, ensuring the confidentiality and integrity of sensitive healthcare information while minimizing the cost and increasing the portability specialized for healthcare and emergency environments. The experimental results underscore the accuracy of up to 93.6% for predicting various attacks based on the results demonstrate a zero-day detection accuracy simulated using the CICIoT2023 dataset and reduces the cost by a factor of x10. The significance of our approach is in fortifying the security posture of IoT devices and maintaining a robust implementation of trustful healthcare systems.

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