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The results indicate that the proposed CNN–MHSA framework offers a robust benchmark for secure healthcare communication and can successfully identify MQTT threats in IoMT networks.
This review critically analyzes the cybersecurity research published over the past few years on cyber threats across the various layers of the IIoT architecture, publicly available cybersecurity datasets, evaluation practices, and AI-based intrusion detection methods to provide a pathway toward resilient, adaptive, and...
This study presents future directions for dataset design aligned with the requirements of next-generation IDSs by highlighting digital twin-based IIoT environments, edge-cloud collaborative data generation, sequential traffic modeling, and explainability-oriented annotations that can ensure robust, trustworthy, and dep...
The proposed Self-Healing IoT-Optimized Random Forest framework provides stable and safety-oriented intrusion detection capability under heterogeneous IoMT deployment conditions while maintaining strict testing independence and robust performance under rigorous evaluation settings.
An Enhanced Multi-Model Ensemble Network Intrusion Detection System (EME-NIDS), a deep meta-learning system that combines five different heterogeneous learning paradigms, including Convolutional Neural Networks, Dense Neural Networks, Transformers, XGBoost, and Random Forests is introduced.
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