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%.

The field of Cyber-Physical Industrial Internet of Things (CPS-IIoT) is rapidly developing, raising significant concerns about cyber-attacks due to the susceptibility of its devices and networking protocols. A breach in one device can compromise the entire system, necessitating robust security solutions. Existing methods fail to address the diversity and compatibility of CPS-IIoT environments. In this research, we propose a new privacy-preservation via Pearson correlation coefficient and agglomerative clustering with Bidirectional long short-term memory (BiLSTM) integrated with a scaled dot-product attention for cyber-attacks detection in CPS-IIoT. The inclusion of agglomerative clustering and scaled dot product attention mechanism in our proposed system is a unique characteristic, specifically tailored for CPS-IIoT contexts. These mechanisms adaptively modify their emphasis to prioritize crucial features within the CPS-IIoT network traffic data, providing additional computational resources to data segments that are likely to include abnormalities and patterns that indicate security issues. This research is the first to investigate cyber-threats within CPS-Industrial IoT. We evaluated the performance of our proposed model by conducting experiments on two relevant datasets: UNSW-NB15, and a novel IIoT dataset named X-IIoTID. The X-IIoTID is a versatile intrusion data designed to accommodate the diversity and compatibility of Industrial IoT systems, regardless of their connectivity and device specifications. The data encompasses the actions of emerging IIoT connectivity protocols, recent device activities, a range of attack types and situations, as well as multiple attack protocols. We used a CPS-IIoT testbed that emulates a real industrial facility to demonstrate our proof of concept. Our system exhibits outstanding performance attaining 99.60% of accuracy, 100% of AUC, 97.98% of recall, 100% of precision, F1 of 98.23%, kappa of 96.07%, and Mathew correlation coefficient of 96.54% on UNSW-NB15. On the representative and realistic X-IIoTID data, our proposed system exhibits outstanding performance attaining 99.99% of accuracy, 100% of AUC, recall of 99.97%, 99.98% of precision, 99.87% of F1-score, 99.97% of kappa, and Mathew correlation coefficient of 99.98%. Additionally, we design SHAPley Additive exPlanations from eXplainable AI to enhance our proposed model interpretability and reliability. The findings revealed that the scaled dot product attention mechanism dramatically boosts model performance, while Pearson correlation and agglomerative clustering safeguard data privacy in CPS-IIoT, surpassing the performance of existing state-of-the-art (SOTA) models. Our model achieves 2.7% higher accuracy than TinyLSTM at only 11% higher energy cost, justifying its use in accuracy-critical CPS-IIoT scenarios.