Open Access

ARTICLE

ExGAME: An Explainable Game Theoretic and Adaptive Intrusion Detection Framework for Human-Centric Medical IoT

Noha Alnazzawi¹, Nazik Alturki^2,*, Umar Mujahid³, Fahad Masood⁴, Jawad Ahmad⁵

1 Computer Science and Engineering Department, Yanbu Industrial College, Royal Commission for Jubail and Yanbu, Yanbu, Saudi Arabia
2 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
3 School of Science and Technology, Georgia Gwinnett College, Lawrenceville, GA, USA
4 Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
5 Cybersecurity Center, Prince Mohammad Bin Fahd University, Alkhobar, Saudi Arabia

* Corresponding Author: Nazik Alturki. Email:

Computer Modeling in Engineering & Sciences 2026, 148(2), 47 https://doi.org/10.32604/cmes.2026.085378

Received 10 May 2026; Accepted 29 June 2026; Issue published 28 August 2026

Abstract

The rapid deployment of Internet of Medical Things (IoMT) devices in current healthcare systems has made it much easier to maintain patient monitoring, make diagnoses, and provide long-distance medical treatment. The interconnection of these devices also creates significant cybersecurity problems, including distributed denial-of-service attacks, data breaches, and network intrusions. High detection accuracy and interpretability are essential for a trustworthy intrusion detection system. This study presents ExGAME, an Explainable Game-Theoretic Artificial Intelligence framework intended for intrusion detection in human-centric IoT networks. The proposed framework combines machine-learning-based anomaly detection with explainable AI and a game-theoretic defense strategy to improve both detection performance and decision-making clarity. A game-theoretic perspective is used to explain the interaction of the attackers and defenders. Experiments have been performed using IoT-23, Bot-IoT, CICIDS2017, UNSW-NB15, WUSTL-EHMS-2020, and MedBIoT datasets. Results achieved an accuracy range of upto 98% in different attack scenarios. The results show that packet rate and traffic flow characteristics are very important for distinguishing between normal and abnormal network activities. The proposed ExGAME architecture makes detection more transparent while enabling effective intrusion localization. This research aids in the creation of secure, comprehensible, and flexible protection mechanisms for future healthcare IoT systems.

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Keywords

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