The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it also introduces serious cybersecurity and patient safety concerns as attackers increasingly exploit new methods and emerging vulnerabilities to infiltrate IoMT networks. This paper proposes a novel Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for detecting a wide range of cyberattacks targeting IoMT networks. The TM is a rule-based and interpretable machine learning (ML) approach that models attack patterns using propositional logic. Extensive experiments conducted on the CICIoMT-2024 dataset, which includes multiple IoMT protocols and cyberattack types, demonstrate that the proposed TM-based IDS outperforms traditional ML classifiers. The proposed model achieves an accuracy of 99.5\% in binary classification and 90.7\% in multi-class classification, surpassing existing state-of-the-art approaches. Moreover, to enhance model trust and interpretability, the proposed TM-based model presents class-wise vote scores and clause activation heatmaps, providing clear insights into the most influential clauses and the dominant class contributing to the final model decision.

翻译：医疗物联网的快速普及通过实现医疗设备、系统和服务间的无缝连接正在变革医疗行业。然而，这也引发了严重的网络安全和患者安全问题，因为攻击者不断利用新方法和新兴漏洞入侵医疗物联网网络。本文提出了一种基于Tsetlin机器的新型入侵检测系统，用于检测针对医疗物联网网络的各类网络攻击。Tsetlin机器是一种基于规则、可解释的机器学习方法，通过命题逻辑对攻击模式进行建模。在包含多种医疗物联网协议和网络攻击类型的CICIoMT-2024数据集上进行的广泛实验表明，所提出的基于Tsetlin机器的入侵检测系统性能优于传统机器学习分类器。该模型在二分类任务中达到99.5%的准确率，多分类任务中达到90.7%的准确率，超越了现有最先进方法。此外，为增强模型可信度和可解释性，该模型展示了类别级投票分数和子句激活热力图，清晰揭示了影响最终模型决策的最关键子句和主导类别。