On-Device Interpretable Tsetlin Machine-Based Intrusion Detection for Secure IoMT

TL;DR

This paper introduces an on-device, interpretable Tsetlin Machine-based intrusion detection system for IoMT networks, achieving high accuracy and transparency in identifying cyberattacks in real-time.

Contribution

It presents a novel, rule-based Tsetlin Machine model for IoMT intrusion detection that combines high performance with interpretability and supports edge deployment.

Findings

Achieved 97.83% classification accuracy on MedSec-25 dataset.

Outperformed existing ML models and state-of-the-art methods.

Enabled real-time on-device intrusion detection with explainability features.

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.