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University of Limerick

Academic institutioneurope · ie
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Research library40linked papers
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Selected work

Representative Papers

Deep Learning for Cyber Threat Detection and Mitigation in Healthcare-IoT

Jul 31, 2026

This study addresses the vulnerability of resource-constrained devices in healthcare Internet of Things (H-IoT) systems to cyberattacks such as DDoS, man-in-the-middle (MITM), and selective forwarding, which pose serious risks to patient safety. Existing intrusion detection approaches are often hindered by low-quality datasets and computationally intensive models. To overcome these limitations, this work introduces a novel framework that integrates physiological signals with network traffic features and constructs three realistic multi-attack H-IoT datasets using Cooja and ns-3 simulations. A lightweight temporal convolutional network (TCN/Res-TCN) is proposed, augmented with a dynamic thresholding mechanism and optimized monitoring frequency. The model is quantized via TensorFlow Lite and deployed on a Raspberry Pi 4. Experimental results demonstrate real-time attack detection with low latency and power consumption under MQTT/UDP protocols, enabling efficient edge-based security for H-IoT environments.

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Recent publications

Latest Papers

Deep Learning for Cyber Threat Detection and Mitigation in Healthcare-IoT

Jul 31, 2026

This study addresses the vulnerability of resource-constrained devices in healthcare Internet of Things (H-IoT) systems to cyberattacks such as DDoS, man-in-the-middle (MITM), and selective forwarding, which pose serious risks to patient safety. Existing intrusion detection approaches are often hindered by low-quality datasets and computationally intensive models. To overcome these limitations, this work introduces a novel framework that integrates physiological signals with network traffic features and constructs three realistic multi-attack H-IoT datasets using Cooja and ns-3 simulations. A lightweight temporal convolutional network (TCN/Res-TCN) is proposed, augmented with a dynamic thresholding mechanism and optimized monitoring frequency. The model is quantized via TensorFlow Lite and deployed on a Raspberry Pi 4. Experimental results demonstrate real-time attack detection with low latency and power consumption under MQTT/UDP protocols, enabling efficient edge-based security for H-IoT environments.

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