Deep Learning for Cyber Threat Detection and Mitigation in Healthcare-IoT
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...
| Authors | Mirza Akhi |
|---|---|
| Primary category | cs.CR |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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