An Adaptive Intrusion Detection System for Securing the Internet of Medical Things Using Deep Learning - Abdullah M. Albarrak (EAI Endorsed Transactions on Internet of Things, 2026)
INTRODUCTION: The Internet of Medical Things (IoMT) has expanded rapidly, with a growing number of medical devices becoming interconnected and increasingly integral to healthcare delivery. However, this expansion has also introduced significant cybersecurity risks, making IoMT networks vulnerable to sophisticated cyber-attacks that threaten patient data confidentiality and system reliability. OBJECTIVES: This study aims to develop a robust intrusion detection framework capable of accurately identifying both known and unknown cyber-attacks in IoMT environments while minimizing false positives and false negatives. METHODS: The proposed framework employs a Capsule Neural Network (CapsNet) to effectively capture spatial hierarchies and part–whole relationships in network traffic data. Additionally, the Theory of Association (TOA) is utilized for batch-size hyperparameter tuning to enhance learning efficiency and detection performance. The model is evaluated using standard performance metrics to assess its effectiveness in detecting malicious traffic. RESULTS: Experimental results demonstrate that the proposed Intrusion Detection System (IDS) achieves an accuracy of 98.37%, precision of 98.57%, recall of 98.17%, and an F1 score of 98.37%. These results indicate strong real-time detection capability with minimal false positives and false negatives. CONCLUSION: The findings highlight the effectiveness of integrating deep learning techniques, particularly CapsNet with TOA-based optimization, in strengthening cybersecurity for IoMT networks. The proposed IDS provides a secure and efficient solution for protecting healthcare data and ensuring patient confidentiality, offering a promising approach to enhancing the security and performance of healthcare IoMT systems.