MSCA-Net: A Multi-Scale Depthwise Attention Network for Multi-Class Intrusion Detection in Internet of Medical Things

The Internet of Medical Things (IoMT) enables real-time monitoring and decision support systems in healthcare. However, due to their heterogeneous structure, limited resources, and high criticality, IoMT networks are vulnerable to cyberattacks. This situation increases the need for low-latency, high-accuracy, and generalizable attack detection systems. In this experimental study, the Multi-Scale Depthwise Channel Attention Network (MSCA-Net) model is proposed for multi-class attack detection in IoMT environments. The model consists of three core components: multi-scale depthwise separable convolutions to capture traffic patterns across different time scales, a squeeze-and-excitation-based channel attention mechanism that adaptively weights discriminative features, and a lightweight unidirectional LSTM layer that models temporal dependencies. This architecture enables effective representation learning with low parameter costs. The proposed model was evaluated on the WUSTL-EHMS-2020 and CICIoMT2024 datasets. On the CICIoMT2024 dataset, it achieved 99.75% accuracy and a weighted F1 score of 99.77% in a 6-class scenario. It has also demonstrated competitive results in 19-class fine-grained classification. Experimental comparisons show that MSCA-Net offers a better performance-to-cost trade-off compared to nine different baseline models. Furthermore, it demonstrates a speed advantage of up to two times in inference time. The results obtained at the conclusion of the experimental study demonstrate that the proposed approach effectively addresses the challenges of multi-scale feature extraction, class imbalance, and computational efficiency. Furthermore, the model appears to offer a viable solution for real-time attack detection in IoMT environments.