🤖 AI Summary
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.
📝 Abstract
Cybersecurity is a fundamental requirement for protecting wearable devices used in healthcare Internet of Things (H-IoT) systems. Security failures in these resource-constrained systems directly compromise patient safety. Physiological data and network traffic are frequent targets of cyberattacks in H-IoT environments. To address these risks, deep learning-based cybersecurity mechanisms for H-IoT often involve complex architectures with large parameter counts. Existing datasets are also rarely assessed for quality, limiting their applicability. However, this research addresses these challenges by developing multiple realistic datasets and proposing lightweight deep learning models, namely the Temporal Convolutional Network (TCN) and Residual TCN (Res-TCN), for H-IoT. It includes two binary classification datasets for Distributed Denial of Service (DDoS) attacks and a multiclass dataset representing Selective Forwarding (SF), Man-in-the-Middle (MITM), and DDoS attacks. The datasets UL-ECE-MQTT-DDoS-H-IoT2025 and UL-ECE-UDP-DDoS-H-IoT2025 are generated in Cooja and ns-3 to capture transmission behaviours and protocol variations. The third dataset, UL-ECE-MultiAttack-H-IoT2025, integrates physiological and network features to represent multiple cyber threats in H-IoT. Building on this, the TCN model is designed to detect and mitigate DDoS attacks over the MQTT and UDP-based datasets. It incorporates a monitoring frequency-based detection mechanism and a dynamic threshold-based mitigation strategy. To enable edge deployment, the model is quantised and converted into TensorFlow Lite (TFLite) for real-time DDoS detection on Raspberry Pi 4, achieving low latency and power-efficient operation in H-IoT. This thesis establishes a deep learning-based cybersecurity defence mechanism encompassing realistic dataset generation, lightweight model design, and edge deployment for securing H-IoT systems.