XSRU-IoMT: Explainable simple recurrent units for threat detection in Internet of Medical Things networks

Abstract The Internet of Medical Things (IoMT) is increasingly replacing the traditional healthcare systems. However, less focus has been paid to their security against cyber-threats in the implementation of the IoMT and its networks. One of the key reasons can be the challenging task of optimizing typical security solutions to the IoMT networks. And despite the rising admiration of machine learning and deep learning methods in the cyber-security domain (e.g., a threat detection system), most of these methods are acknowledged as a black-box model. The explainable AI (XAI) has become progressively vital to understand the employed learning models to improve trust level and empower security experts to interpret the prediction decisions. The authors propose a highly efficient model named XSRU-IoMT, for effective and timely detection of sophisticated attack vectors in IoMT networks. The proposed model is developed using novel bidirectional simple recurrent units (SRU) using the phenomenon of skip connections to eradicate the vanishing gradient problem and achieve a fast training process in recurrent networks. We also explore the concepts of XAI to improve trust level by providing explanations of the predictive decisions and enabling humans and security experts to understand the causal reasoning and underlying data evidence. The evaluation results on the ToN_IoT dataset demonstrate the effectiveness and superiority of the proposed XSRU-IoMT model as compared to the state-of-the-art compelling detection models, suggesting its usefulness as a viable deployment model in real-IoMT networks.