## 2 Critical Review  
The authors in [30] introduced the IoT-enabled smart transportation and healthcare system which has gained popularity among academics and practitioners. In this research the authors have resolved the challenges of object detection and semantic segmentation in the context of IoT by using ML techniques. The author of [31] discusses improving the new enhancements in health wearable devices that are challenged by upgrading with innovative technology and resources that can be monitored periodically and regularly. The author offers a technique for monitoring IoT applications by using ML techniques to create an advanced tracking automation system and make decisions for proper diagnosis [32]. Concentrated on AD, which is characterized as a significant interest in detecting known or undiscovered anomalous complicated patterns of different destructive assaults in network protocols. The author chose the problem of a non-trivial algorithm that classifies data instances and where various application areas have different perspectives on anomalies. In the medical field, for example, a minor deviation is considered an anomaly. The author re-solved these challenges by utilizing ML, which is one of the most important techniques for AD since it automatically learns the system's information or actions and is capable of recognizing complicated patterns. As a result, the computer's functionality is threatened. The sophistication of IoT networks exposes their systems to dangerous vulnerabilities. Security flaws and abnormalities in IoT devices have become more common in recent years [33; 34]. Several comparable efforts have been done in IoT, and academics are still researching in this field [35]. Created a detector and firewall to detect an abnormality at an IoT site. Clustering methods such as K-Means were used in this study for various IoT sectors. In clustering, different clusters were brought together if the center is inside three times the average deviation distance. A detailed explanation of a home automation system in which security breaches were uncovered utilizing a variety of ML approaches is presented in [36]. They mostly detailed a Denial of Service (DoS) attack on a basic IoT site. According to [37] a detector for On and Off attacks by a fraudulent network node in an industrial IoT site was presented. They suggested that an IoT network may be assaulted by a compromised node when it is operational or turned On. Moreover, while the malicious node is in the dormant or Off state, the IoT network works normally. For the identification of such an abnormality, the system was created using a light probe routing strategy, with each neighbor node's trust estimation computed. The current security concern while building embedded technology for the IoT was described in [38]. Monitoring data flow between the IoT device's physical, logical, and virtual parts was also difficult. To overcome these concerns, the authors of this paper suggested using digital watermarking [39] Published a paper describing an IoT intrusion detection system. Several ML classifiers have been successfully utilized to identify network monitoring probing and fundamental sorts of DoS attacks. Investigated the detection of problems in healthcare analytic using IoT [40]. This paper also offered a model for identifying heart abnormalities using a smartphone. IoT sensors, medical image analysis biomedical signal analysis, big data mining, and predictive analytic was applied to uncover abnormalities in healthcare. Several assaults on IoT networks may be identified in the literature, ranging from examining the impact of some well attacks in IoT settings [41] to suggesting unique threats targeting IoT networks, protocols, or nodes [42]. Threats to infrastructure and networks are detected using ML algorithms.