Institution profile

University of Limerick

Academic institutioneurope · ie
Official website
Research library39linked papers
Opportunities0open roles
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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Toward Optimal Adenovirus Detection Using YOLO26

Jul 20, 2026

This study addresses the insufficient accuracy and robustness in adenovirus detection within transmission electron microscopy (TEM) images by systematically evaluating, for the first time, the performance of four advanced data augmentation strategies—NAS, GAS, GMAS, and DAS—across different YOLOv8 model scales. Under unified training conditions, the authors generated YOLO-compatible bounding boxes through refined re-annotation of adenovirus particle locations and established a standardized preprocessing pipeline. Experimental results demonstrate that specific augmentation techniques, particularly DAS, substantially improve detection accuracy. The study identifies the optimal combination of model architecture and augmentation strategy, offering an efficient and reliable technical pathway for automated virus particle detection in TEM imagery.

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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.

0 citationsRead paper

Toward Optimal Adenovirus Detection Using YOLO26

Jul 20, 2026

This study addresses the insufficient accuracy and robustness in adenovirus detection within transmission electron microscopy (TEM) images by systematically evaluating, for the first time, the performance of four advanced data augmentation strategies—NAS, GAS, GMAS, and DAS—across different YOLOv8 model scales. Under unified training conditions, the authors generated YOLO-compatible bounding boxes through refined re-annotation of adenovirus particle locations and established a standardized preprocessing pipeline. Experimental results demonstrate that specific augmentation techniques, particularly DAS, substantially improve detection accuracy. The study identifies the optimal combination of model architecture and augmentation strategy, offering an efficient and reliable technical pathway for automated virus particle detection in TEM imagery.

0 citationsRead paper