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Attention-enhanced deep learning model for MQTT intrusion detection in internet of medical things
Siddhartha Singhal1, Kakelli Anil Kumar1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Introduction:
The increasing utilization of the Message Queuing Telemetry Transport (MQTT) protocol in IoMT has created a high demand for efficient intrusion detection systems (IDSs).
Methods:
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.
Results:
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.
Discussion:
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.