The usage of IoT in the medical field, often referred to as IoMT, plays a vital role in facilitating the exchange of sensitive data among medical devices. This capability significantly contributes to enhancing the quality of patient care. However, it comes with privacy issues that compromise the security of the data collected by medical sensors, making them vulnerable to potential cyber threats such as data modification, replay attacks, etc. These attacks can lead to significant data loss or unauthorized alterations. Machine learning, particularly in cyber-attack detection systems, is crucial for identifying and classifying such attacks. Yet, the main challenge lies in adapting to the dynamic and unpredictable nature of malicious attacks and creating scalable solutions to combat them. The objective of this paper is to detect cybersecurity threats, with a particular focus on man-in-the-middle attacks that occur within the IoMT communication network. The study utilizes principal component analysis (PCA) for feature reduction and employs multi-layer perceptron to classify unforeseen cyber-attack IoT-based healthcare devices. The study evaluates the effectiveness of this proposed strategy using real-time data from the St. Louis Enhanced Healthcare Monitoring System (WUSTL-EHMS). The findings indicate that the multi-layer perceptron outperforms other tested classifiers, achieving an accuracy score of 96.39%, while also improving the performance by reducing the time complexity.

In crucial applications, sensor node coverage of the objective zone must be stabilized in Wireless Sensor Networks (WSN). A network with holes in coverage is more susceptible to node failures or malicious attacks. According to the total number of hops used to transport data, nodes may calculate their distance from the sink node. A coverage hole may be present if a node notices a much higher hop count than its neighbors. The network becomes more robust and resilient to diverse problems by proactively recognizing and correcting coverage holes. Coverage hole identification aids in the efficient use of network resources. By identifying places with poor coverage, resources such as electricity and bandwidth may be efficiently deployed to increase coverage in specific areas or extend the network lifetime overall. However, some node sensors die while the network operates due to energy restrictions, which may disturb the inclusion of the objective zone, resulting in a coverage hole. Due to limited battery life, the existence of impediments and physical damage to sensor nodes, coverage holes may emerge in sensor networks. Early identification of coverage holes enables prompt maintenance and troubleshooting, which minimizes the need for future major and expensive replacements or reconfigurations. The loss on the region of interest may be calculated by locating the coverage holes and identifying the malfunctioning node that created it. This article discusses many coverage-hole-detecting methods, classification approaches, and different performance comparison assessments. Compared to conventional techniques for detecting coverage holes, the investigated methods contribute to the universal viewpoint on holes and compute the number of holes quite precisely.