Abstract

The proliferation of Internet of Things devices in healthcare, specifically the Internet of Medical Things, has revolutionized patient care and health monitoring systems. Integrating these interconnected medical devices introduces unprecedented security challenges, necessitating robust Intrusion Detection Systems (IDS) to safeguard patient data and healthcare infrastructure. To protect the IoMT devices from numerous malicious attacks, researchers have developed numerous Intrusion Detection Systems, but the development of a practical and real-time IDS remains a challenge. Our proposed IDS addresses this gap and surpasses state-of-the-art IDS techniques for IoMT networks. In this research paper, we have proposed a novel IDS approach for IoMT, leveraging a Hybrid Deep Learning technique to enhance detection accuracy and efficiency. By combining the strengths of the Gated Recurrent Unit (GRU) and Attention Mechanism, the proposed IDS achieves superior performance in detecting anomalous activities in medical networks. The proposed IDS model was evaluated on two publicly available benchmark intrusion datasets and achieved 99.99 % accuracy on the ICU Healthcare Dataset and 98.94 % accuracy on the NF-TON-IoT Dataset. Precision, Recall, F1-Score metrics, and ROC-AUC for the proposed model are promising. To show how effectively the model performed in noisy environments, we also added noise to the features. Moreover, we used the K-Fold Cross Validation Technique to cross-validate the model's performance on both datasets, ensuring the reliability and applicability of the suggested IDS model for IoMT networks.

Key words: Internet of Medical Things, Intrusion Detection System, Principal Component Analysis, Deep Learning, Gated Recurrent Unit, Attention Mechanism, K-Fold Cross Validation