用于医疗物联网网络威胁检测与缓解的深度学习
Deep Learning for Cyber Threat Detection and Mitigation in Healthcare-IoT
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中文总结 AI 辅助
本研究针对医疗物联网（H-IoT）的网络威胁，构建三类真实数据集，提出轻量型TCN与Res-TCN模型，经量化后部署于树莓派4实现DDoS攻击的低延迟高能效检测与缓解，为H-IoT提供深度学习安全防御方案。
中文摘要 AI 辅助
网络安全是保护医疗物联网（H-IoT）系统中可穿戴设备的基本要求，这类资源受限系统的安全故障会直接威胁患者安全。生理数据和网络流量是H-IoT环境中网络攻击的常见目标。为应对这些风险，H-IoT的基于深度学习的网络安全机制通常包含参数数量庞大的复杂架构，而现有数据集的质量评估不足，限制了其适用性。本研究通过构建多个真实数据集并提出轻量型深度学习模型——时间卷积网络（TCN）和残差时间卷积网络（Res-TCN）来解决这些挑战，用于H-IoT场景。研究包含两个针对分布式拒绝服务（DDoS）攻击的二分类数据集，以及一个代表选择性转发（SF）、中间人（MITM）和DDoS攻击的多分类数据集。数据集UL-ECE-MQTT-DDoS-H-IoT2025和UL-ECE-UDP-DDoS-H-IoT2025在Cooja和ns-3中生成，以捕捉传输行为和协议差异；第三个数据集UL-ECE-MultiAttack-H-IoT2025整合了生理和网络特征，代表H-IoT中的多种网络威胁。在此基础上，TCN模型被设计用于检测和缓解基于MQTT和UDP数据集的DDoS攻击，它采用基于监测频率的检测机制和基于动态阈值的缓解策略。为支持边缘部署，该模型被量化并转换为TensorFlow Lite（TFLite），用于树莓派4（Raspberry Pi 4）上的实时DDoS检测，在H-IoT中实现了低延迟和高能效的运行。本研究建立了一套基于深度学习的网络安全防御机制，涵盖真实数据集生成、轻量型模型设计和边缘部署，以保障H-IoT系统的安全。
英文摘要
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.
发表机构
- University of Limerick（利默里克大学）
机构由 AI 辅助整理，请以论文原文为准。
