Scholarlink

<p>Intrusion detection in Internet of Things (IoT) networks, particularly in healthcare settings, poses critical challenges due to latency constraints, limited resources, and the need for trustworthy auditing in distributed environments. Centralized detection models often fail to deliver timely or scalable responses under real-world IoT conditions.&#xa0;This study proposes a hybrid fog–cloud architecture tailored for healthcare-oriented IoT threat detection, incorporating blockchain-based auditability. The architecture utilizes fog- and cloud-level XGBoost classifiers trained on BoT-IoT and ToN-IoT datasets, with SMOTE applied to mitigate class imbalance. A lightweight blockchain module is integrated at the fog layer to log predictions in real-time for tamper-evident traceability. Simulations were performed using 50 fog-predicted events to evaluate performance, energy usage, and blockchain entropy.&#xa0;The system achieved an average block creation time of under 20 ms with minimal CPU and memory overhead. It also demonstrated robustness against tampering, preserving data integrity. The fog-level model achieved competitive metrics (AUC = 1, F1-score = 98.70%, Accuracy = 99.80%) compared to the cloud model, while outperforming it in terms of response latency and localized decision-making.&#xa0;The proposed blockchain-integrated fog–cloud framework enables secure, low-latency, and scalable threat detection for healthcare IoT systems, offering a promising foundation for privacy-aware edge intelligence.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Energy-efficient threat detection in IoT healthcare using AI and blockchain-enhanced fog–cloud architecture

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

查看全文
  • Malak Alamri,
  • Noshina Tariq,
  • Mamoona Humayun,
  • Menwa Alshammeri

摘要

Intrusion detection in Internet of Things (IoT) networks, particularly in healthcare settings, poses critical challenges due to latency constraints, limited resources, and the need for trustworthy auditing in distributed environments. Centralized detection models often fail to deliver timely or scalable responses under real-world IoT conditions. This study proposes a hybrid fog–cloud architecture tailored for healthcare-oriented IoT threat detection, incorporating blockchain-based auditability. The architecture utilizes fog- and cloud-level XGBoost classifiers trained on BoT-IoT and ToN-IoT datasets, with SMOTE applied to mitigate class imbalance. A lightweight blockchain module is integrated at the fog layer to log predictions in real-time for tamper-evident traceability. Simulations were performed using 50 fog-predicted events to evaluate performance, energy usage, and blockchain entropy. The system achieved an average block creation time of under 20 ms with minimal CPU and memory overhead. It also demonstrated robustness against tampering, preserving data integrity. The fog-level model achieved competitive metrics (AUC = 1, F1-score = 98.70%, Accuracy = 99.80%) compared to the cloud model, while outperforming it in terms of response latency and localized decision-making. The proposed blockchain-integrated fog–cloud framework enables secure, low-latency, and scalable threat detection for healthcare IoT systems, offering a promising foundation for privacy-aware edge intelligence.