Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms

Information and communication technology (ICT) advancements have altered the entire computing paradigm. As a result of these improvements, numerous new channels of communication are being created, one of which is the Internet of Things (IoT). The IoT has recently emerged as cutting-edge technology for creating smart environments. The Internet of Medical Things (IoMT) is a subset of the IoT, in which medical equipment exchange information with each other to exchange sensitive information. These developments enable the healthcare business to maintain a higher level of touch and care for its patients. Security is seen as a significant challenge in whatsoever technology’s reliance based on the IoT. Security difficulties occur owing to the various potential attacks posed by attackers. There are numerous security concerns, such as remote hijacking, impersonation, denial of service attacks, password guessing, and man-in-the-middle. In the event of such attacks, critical data associated with IoT connectivity may be revealed, altered, or even rendered inaccessible to authorized users. As a result, it turns out to be critical to safeguard the IoT/IoMT ecosystem against malware assaults. The main goal of this study is to demonstrate how a deep recurrent neural network (DRNN) and supervised machine learning models (random forest, decision tree, KNN, and ridge classifier) can be utilized to develop an efficient and effective IDS in the IoMT environment for classifying and forecasting unexpected cyber threats. Preprocessing and normalization of network data are performed. Following that, we optimized features using a bio-inspired particle swarm algorithm. On the standard data for intrusion detection, a thorough evaluation of experiments in DRNN and other SML is performed. It was established through rigorous testing that the proposed SML model outperforms existing approaches with an accuracy of 99.76%.