Open Access

ARTICLE

Future-Proofing CIA Triad with Authentication for Healthcare: Integrating Hybrid Architecture of ML & DL with IDPS for Robust IoMT Security

Saad Awadh Alanazi¹, Fahad Ahmad^2,3,*

1 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Aljouf, Saudi Arabia
2 School of Computing, Faculty of Technology, University of Portsmouth, Winston Churchill Ave, Southsea, Portsmouth, PO1 2UP, UK
3 Portsmouth Artificial Intelligence and Data Science Center, University of Portsmouth, Winston Churchill Ave, Southsea, Portsmouth, PO1 2UP, UK

* Corresponding Author: Fahad Ahmad. Email:

Computers, Materials & Continua 2025, 85(1), 769-800. https://doi.org/10.32604/cmc.2025.066753

Received 16 April 2025; Accepted 20 June 2025; Issue published 29 August 2025

Abstract

This study presents a comprehensive and secure architectural framework for the Internet of Medical Things (IoMT), integrating the foundational principles of the Confidentiality, Integrity, and Availability (CIA) triad along with authentication mechanisms. Leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, the proposed system is designed to safeguard Patient-Generated Health Data (PGHD) across interconnected medical devices. Given the increasing complexity and scale of cyber threats in IoMT environments, the integration of Intrusion Detection and Prevention Systems (IDPS) with intelligent analytics is critical. Our methodology employs both standalone and hybrid ML & DL models to automate threat detection and enable real-time analysis, while ensuring rapid and accurate responses to a diverse array of attacks. Emphasis is placed on systematic model evaluation using detection metrics such as accuracy, False Alarm Rate (FAR), and False Discovery Rate (FDR), with performance validation through cross-validation and statistical significance testing. Experimental results based on the Edge-IIoTset dataset demonstrate the superior performance of ensemble-based ML models such as Extreme Gradient Boosting (XGB) and hybrid DL models such as Convolutional Neural Networks with Autoencoders (CNN+AE), which achieved detection accuracies of 96% and 98%, respectively, with notably low FARs. These findings underscore the effectiveness of combining traditional security principles with advanced AI-driven methodologies to ensure secure, resilient, and trustworthy healthcare systems within the IoMT ecosystem.

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Keywords

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