The rapid growth of IoT has significantly changed modern technology by allowing devices, systems, and services to connect easily across different areas. Due to the growing popularity of Internet of Things (IoT) devices, attackers focus more and more on finding new methods, ways, and vulnerabilities to penetrate IoT networks. Although IoT devices are utilized across a wide range of domains, the Internet of Medical Things (IoMT) holds particular significance due to the sensitive and critical nature of medical information. Consequently, the security of these devices must be treated as a paramount concern within the IoT landscape. In this paper, we propose a novel approach for detecting various intrusion attacks targeting Internet of Medical Things (IoMT) devices, utilizing an enhanced version of the LSTM deep learning algorithm. To evaluate and compare the proposed algorithm with other methods, we used the CICIoMT2024 dataset, which encompasses various types of equipment and corresponding attacks. The results demonstrate that the proposed novel approach achieved an accuracy of 98% for 19 classes, which is remarkably high for classifications and presents a significant and promising outcome for IoMT environments.

Detecting changes in a landscape is a critical issue for several evolutionary dynamic optimization techniques. This is because most of these techniques have steps to be taken as a response to the environmental changes. It may not be feasible for most of the real world problems to know a priori when a change occurs; therefore, explicit schemes should be proposed to detect the points in time when a change occurs. Although there are both sensor-based and population-based detection schemes presented in the literature for single objective dynamic optimization problems, there are no such efforts for the dynamic multi-objective optimization problems (DMOPs). This paper proposes a set of novel sensor-based change detection schemes for DMOPs. An empirical study is presented for validating the performance of the proposed detection schemes by using eight test problems which have different types and characteristics. Additionally, the proposed change detection schemes are incorporated into a dynamic multi-objective evolutionary algorithm to validate the effectiveness of the proposed change detection schemes.