Advanced hybrid malware identification framework for the Internet of Medical Things, driven by deep learning

Abstract The Internet of Things (IoT) effortlessly enables communication between items on the World Wide Web and other systems. This extensive use of IoTs has created new services and automation in numerous industries, enhancing the standard of living, especially in healthcare. Internet of Medical Things (IoMT) adoption has been beneficial during pandemic conditions by enabling remote patient monitoring and therapy. Nevertheless, the excessive use of IoMT has raised security concerns as it can compromise critical data. This breach in security can result in an inaccurate diagnosis or violate privacy. This research presents a novel approach to hybrid deep learning‐based detection of malware solutions for the IoT. This study uses RNN‐Bi‐LSTM to detect and extract significant features related to an already existing dataset. The proposed model exhibits a detection accuracy of 98.38% when evaluated using these existing datasets. Statistical tests like Mathew co‐relation and Log Loss also indicated reliability of proposed framework. The distinguished feature of our framework is its ability to combine complex deep learning models for IoMT security, which is of economic and scientific importance. It certainly offers a reliable solution for healthcare applications that rely on real‐time functionality and dependency on IoMT systems.