Deep Learning-Based Network Intrusion Detection System for Internet of Medical Things

Abstract

This article presents a deep learning-based approach for network-based intrusion detection in the Internet of medical things (IoMT) systems using features of network flows and patient biometrics. The proposed approach effectively learns optimal feature representation by passing the information of network flows and patient biometrics into more than one hidden layer of deep learning. The network includes a global attention layer which helps to effectively extract the optimal features from the spatial and temporal features of deep learning. To avoid data imbalance, a cost-sensitive learning approach is integrated into the deep learning model. The proposed model showed a 10-fold cross-validation accuracy of 95 percent on network features, 89 percent on patient biometrics, and 99 percent on combined features. In addition to the IoMT environment, the robustness and generalization ability of the proposed model is shown by conducting experiments on other network-based intrusion datasets. The proposed approach outperformed the existing methods in all the test cases mainly showing a 3.9 percent higher accuracy on the IoMT intrusion dataset. The proposed model can be used as an IoMT network monitoring tool to safeguard the IoMT devices and networks from attackers inside the healthcare and medical environment.