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Residual temporal CNNs for emerging cyber threat detection in healthcare IoT
Date
2026
Abstract
The rapid advancement of Internet of Things (IoT) technologies has accelerated the emergence of healthcare-IoT (H-IoT) systems. These systems rely on wearable devices to monitor patient vitals and enable timely alerts in precision healthcare settings. Despite these benefits, a single H-IoT network topology might be exposed to multiple simultaneous threats, particularly those attacks designed to manipulate medical sensor data at the application layer. This poses significant challenges for realtime detection and classification of diverse attack behaviors. To address this, a realistic application-layer attack model is developed using the Cooja simulator, modeling H-IoT nodes that track body temperature, oxygen level, and heart rate under concurrent Selective Forwarding (SF), Man-in-the-Middle (MITM), and Distributed Denial of Service (DDoS) attacks. Based on this setup, a dataset is generated to train the proposed deep learning model. This research proposes a deep learning model, a Residual-Temporal Convolutional Network (Res-TCN), designed to classify multiclass attacks while maintaining low latency per sample in H-IoT environments. It also uses the Synthetic Minority Oversampling Technique (SMOTE) during training to mitigate class imbalance and reduce overfitting. The proposed model achieves a high classification accuracy of 99.32% and outperforms traditional ML and DL methods. This demonstrates its effectiveness in real-time decision-making for securing H-IoT systems. Based on these findings, the Res-TCN model is potentially well-suited for deployment in resource-constrained H-IoT environments
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Description
Publisher
Springer Nature
Citation
Discover Internet of Things 6(12)
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Akhi_2026_Residual.pdf
Adobe PDF, 5.45 MB
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Funding Information
Sustainable Development Goals
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License
Attribution-NonCommercial-ShareAlike 4.0 International