INTRUSION DETECTION SYSTEM FOR INTERNET OF MEDICAL THINGS USING GRU WITH ATTENTION MECHANISM-BASED HYBRID DEEP LEARNING TECHNIQUE

(Received: 6-Sep.-2024, Revised: 8-Nov.-2024 , Accepted: 21-Nov.-2024)

Authors Naveen Saran, Nishtha Kesswani,

Keywords #Internet of medical things #Intrusion-detection system #Principal-component analysis #Deep learning #Gated recurrent unit #Attention mechanism #K-fold cross validation

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 (IDSs) 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 an effective 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 Gated Recurrent Unit (GRU) and Attention Mechanism, the proposed IDS achieves superior performance in detecting anomalous activities in medical networks. We evaluated the proposed IDS model on two publicly available benchmark intrusion datasets and achieved 99.99 % accuracy on the ICU Healthcare Dataset and 98.94 % on the NF-TON-IoT Dataset. Precision, Recall, F1-score metrics and ROC-AUC for the proposed model are promising. We also added Noise to the features to show how effectively the model performed in noisy environments. Moreover, we used the K-Fold Cross Validation Technique to cross-va�O�L�Ga�W�H �W�K�H �Po�G�H�O’�V performance on both datasets, ensuring the reliability and applicability of the suggested IDS model for IoMT networks.

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,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 (IDSs) 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 an effective 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 Gated Recurrent Unit (GRU) and Attention Mechanism, the proposed IDS achieves superior performance in detecting anomalous activities in medical networks. We evaluated the proposed IDS model on two publicly available benchmark intrusion datasets and achieved 99.99 % accuracy on the ICU Healthcare Dataset and 98.94 % on the NF-TON-IoT Dataset. Precision, Recall, F1-score metrics and ROC-AUC for the proposed model are promising. We also added Noise to the features to show how effectively the model performed in noisy environments. Moreover, we used the K-Fold Cross Validation Technique to cross-va�O�L�Ga�W�H �W�K�H �Po�G�H�O’�V performance on both datasets, ensuring the reliability and applicability of the suggested IDS model for IoMT networks.},
keywords={Internet of medical things,Intrusion-detection system,Principal-component analysis,Deep learning,Gated recurrent unit,Attention mechanism,K-fold cross validation},
ISSN={2413-9351},
month={}}