Gencer, Burak; Dogan, Gurkan

The diversity of devices in the Internet of Medical Things (IoMT) and Internet of Health Things (IoHT) networks creates significant security vulnerabilities against cyberattacks. Traditional centralized machine learning approaches, however, are not always suitable for these environments due to data privacy regulations. In this study, a new Deep Convolutional Neural Network (DCNN) architecture is proposed for attack detection in IoMT/IoHT networks. The proposed architecture integrates the Residual Squeeze-and-Excitation (RSE) Block, the Squeeze-and-Excitation attention mechanism (SEM), and a one-dimensional multi-pooling approach. The model was evaluated within a federated learning (FL) framework called Federated Medical Intrusion Detection System (FedMedIDS), which preserves data privacy and demonstrates robustness in heterogeneous client scenarios. Four different training strategies—Centralized, FedAvg, FedProx, and Local-Only—were adopted for the evaluations. The proposed model and existing models were tested on the Canadian Institute for Cybersecurity – Internet of Medical Things 2024 (CICIoMT2024) and Edith Cowan University – Internet of Health Things (ECU-IoHT) datasets and compared with current deep learning models. The DCNN proposed for the CICIoMT2024 dataset and a Dirichlet distribution with α = 1.0 achieved F1 scores of up to 99.92% under FedAvg and 94.64% under FedProx with α = 0.5. At α = 0.05, the highest level of heterogeneity, it outperformed the Long Short-Term Memory (LSTM) by as much as 27%. On the ECU-IoHT dataset, F1 scores of up to 89.57% under FedAvg and 89.50% under FedProx were achieved at α = 1.0. This represents an improvement of up to 37% over LSTM at the same level of heterogeneity.

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