Abstract:The rapid evolution of digital health technologies is redefining healthcare services worldwide. The integration of wireless communication and Internet-enabled medical devices within Internet of Medical Things (IoMT) networks enables continuous, real-time patient monitoring. However, this increased connectivity raises cybersecurity and patient safety risks due to increasingly sophisticated cyberattacks. This paper proposes a novel on-device, interpretable Tsetlin Machine (TM)-based Intrusion Detection System (IDS) to identify various phases of cyberattacks in IoMT environments. The TM is a rule-driven and transparent machine learning (ML) approach that represents attack patterns using propositional logic. Extensive evaluations on the MedSec-25 dataset, encompassing various phases of realistic cyberattacks, show that the proposed model outperforms ML models and state-of-the-art methods, attaining a classification performance of 97.83\%. Moreover, the proposed model offers explicit explanations of its decisions to enhance transparency using feature-level contributions, class-wise vote scores, and clause activation heatmaps. Edge deployment (Raspberry Pi) further supports real-time on-device inference and intrusion detection. The combination of interpretability and high performance makes the proposed model well-suited for IoMT healthcare, where trust, reliability, safety, and timely decision-making are critical.
| Comments: | 8 pages, 11 figures, 6 Tables, submitted to IEEE Intelligent Conference on Intelligence and Security Informatics (ISI-2026), Cambridge, UK |
| Subjects: | Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.16707 [cs.CR] |
| (or arXiv:2605.16707v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16707 arXiv-issued DOI via DataCite (pending registration) |
From: Rahul Kumar Jaiswal [view email]
[v1]
Fri, 15 May 2026 23:44:40 UTC (2,880 KB)
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