The increasing utilization of the Message Queuing Telemetry Transport (MQTT) protocol in IoMT has created a high demand for efficient intrusion detection systems (IDSs).
This study proposes a protocol-specific intrusion detection system (IDS) that employs a hybrid deep learning architecture consisting of a one-dimensional convolutional neural network along with a multi-head self-attention mechanism (CNN-MHSA). The architecture employs a convolutional feature-extraction module to derive local traffic features and a multi-head self-attention mechanism to capture long-range feature correlations. The proposed approach is evaluated on the CICIoMT2024 dataset, focusing mainly on the MQTT attack scenarios that are framed as a binary classification problem, in which five distinct MQTT attack variants (DoS-Connect Flood, DDoS-Connect Flood, DoS-Publish Flood, DDoS-Publish Flood, and Malformed Data) were consolidated into a single attack class against benign traffic.
The test results show an accuracy of 99.40%, an F1-score of 99.52%, and an AUC of 0.9998, respectively, for the proposed method.
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 operationally deployable cybersecurity solutions for next-generation IIoT.
Siddhartha Singhal, Kakelli Anil Kumar· Frontiers in Big Data· 0 citations
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 deployment-ready IDS solutions.
Dwarsala Sreedhar Reddy, Kakelli Anil Kumar· Frontiers in Big Data· 1 citation
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.
Siddhartha Singhal, Kakelli Anil Kumar· Frontiers in Big Data· 0 citations
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.
