Residual temporal CNNs for emerging cyber threat detection in healthcare IoT
Published in Discover Internet of Things, 2026
Recommended citation: Mirza Akhi, Ciarán Eising, and Lubna Luxmi Dhirani. (2026). "Residual temporal CNNs for emerging cyber threat detection in healthcare IoT." Discover Internet of Things. https://link.springer.com/article/10.1007/s43926-025-00271-w
This paper presents a Residual-Temporal Convolutional Network (Res-TCN) model for detecting emerging cyber threats in Healthcare-IoT (H-IoT) environments. A realistic application-layer attack model is developed using the Cooja simulator to emulate wearable healthcare devices under Selective Forwarding (SF), Man-in-the-Middle (MITM), and Distributed Denial of Service (DDoS) attacks. The proposed model leverages SMOTE to address class imbalance and achieves high multiclass classification accuracy with low latency. The results demonstrate the effectiveness of Res-TCN for real-time cyber threat detection in resource-constrained H-IoT systems.
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Recommended citation:
‘Akhi, Mirza, Ciarán Eising, and Lubna Luxmi Dhirani. (2025). “Securing IoT Using Lightweight TCN for Edge Deployment on Raspberry Pi 4.” IEEE Open Journal of the Communications Society, 7, 442-460.’