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

TL;DR

This paper presents a new machine learning architecture designed to enhance cybersecurity in healthcare IoT devices, achieving high detection accuracy and reducing costs, thereby strengthening trust and security in healthcare systems.

Contribution

Introduces a novel convolutional ML architecture tailored for healthcare IoT security, improving threat detection accuracy and cost-efficiency.

Findings

Achieves up to 93.6% attack prediction accuracy

Simulated zero-day detection using CICIoT2023 dataset

Reduces security implementation costs by a factor of ten

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.