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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 real-time 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.<\/jats:p>","DOI":"10.1007\/s43926-025-00271-w","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T16:55:08Z","timestamp":1767804908000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Residual temporal CNNs for emerging cyber threat detection in healthcare IoT"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4718-187X","authenticated-orcid":false,"given":"Mirza","family":"Akhi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8383-2635","authenticated-orcid":false,"given":"Ciar\u00e1n","family":"Eising","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7654-4117","authenticated-orcid":false,"given":"Lubna Luxmi","family":"Dhirani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"271_CR1","doi-asserted-by":"publisher","first-page":"12709","DOI":"10.1109\/ACCESS.2025.3531659","volume":"13","author":"M Akhi","year":"2025","unstructured":"Akhi M, Eising C, Dhirani LL. 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