Towards advanced AI-based solutions for securing IoMT in smart health information systems

The Internet of Medical Things (IoMT) refers to the number of interconnected medical objects and applications that are essential in contemporary healthcare. Nevertheless, its dependence on interrelated ecosystems subjects it to high risks of cybersecurity, including data breaches, privacy breaches, and malicious attacks that threaten patient safety and system integrity. This paper resolves these issues by presenting a new and enhanced intrusion detection model, BiGRU/RBWK, which is a combination of Bidirectional Gated Recurrent Units (BiGRU) with a Refined Black-winged Kite (RBWK) optimization algorithm. The RBWK algorithm improves on the BiGRU model by optimizing its hyperparameters, resulting in faster convergence and better classification performance. The model uses a powerful preprocessing pipeline, which includes Recursive Feature Elimination (RFE) that is used in conjunction with Support Vector Machines (SVM), which helps to curb the overfitting and lessen the computational cost. The experimental findings with two public datasets, namely WUSTL-EHMS-2020 and ECU-IoHT, indicate that BiGRU/RBWK model displays higher accuracy (95.6% and 93.8% respectively), precision, recall, F1-score, and AUC-ROC than all other currently available methods, including Random Forest, LSTM, CNN, Autoencoder, and CNN-LSTM hybrid models. The suggested framework has a high discrimination capability and can be easily used in real-time applications in the resource-constrained IoMT settings.