Open Access

ARTICLE

An AI-Driven and Risk-Aware Digital Identity Protection Framework for Secure IoMT Environments

Joong-Hyun Park¹, Jiho Choi², Libor Mesicek³, Hoon Ko^2,*

1 Software-Oriented University, Sunmoon University, 70, Sunmoon-ro 221 beon-gil, Tangjeong-myeo, Asan-City, Chungcheonnam-do, Republic of Korea
2 Department of Computer Science & Engineering, Sunmoon University, 70, Sunmoon-ro 221 beon-gil, Tangjeong-myeo, Asan-City, Chungcheonnam-do, Republic of Korea
3 Faculty of Social and Economic Studies, Jan Evangelista Purkyne University, Pasteurova 1, Usti nad Labem, Czech Republic

* Corresponding Author: Hoon Ko. Email:

(This article belongs to the Special Issue: Advances in Cybersecurity for Digital Ecosystems)

Computers, Materials & Continua 2026, 88(3), 102 https://doi.org/10.32604/cmc.2026.084659

Received 27 April 2026; Accepted 11 June 2026; Issue published 23 July 2026

Abstract

With the rapid expansion of the Internet of Medical Things (IoMT), the importance of digital identity–based security has significantly increased. However, conventional static authentication mechanisms are insufficient to effectively address various identity misuse and abuse attacks. In this study, we model digital identity as a dynamic security entity and propose an AI-based framework that integrates a risk scoring model—combining unsupervised anomaly detection with context-aware analysis—and a multi-level risk-adaptive access control mechanism (Permit, Step-Up, Restrict). Experimental results using an extended version of the CERT Insider Threat Dataset tailored for IoMT environments provide proof-of-concept evidence that the proposed method can achieve an AUC of approximately 0.927, orange demonstrating effective discrimination between normal and malicious behavioral patterns. Furthermore, the framework maintains a low False Restriction Rate of around 1.605% while still detecting attacks at a meaningful level, thereby achieving a balance between security and usability. This study highlights the feasibility of a risk-adaptive digital identity protection framework that dynamically evaluates digital identity and adaptively responds based on risk levels in IoMT environments.

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Keywords

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