## 1 Introduction  
IoT devices represent a paradigm shift in the way we interact digitally with the physical world, as it enables the connectivity and communication between a vast array of devices and systems through a communication network. These interconnected devices, which can range from simple sensors to complex machinery, are equipped with the ability to gather, process, and transmit data, allowing them to sense and respond to their surroundings in an autonomous manner. The IoT shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones in an activity-generated environment under existing networks through embedded systems. Embedded systems provide the essential combination of hardware and firmware, along with Internet access, to execute respective tasks [1]. The key features of IoT devices comprise their abilities of actuation, processing, connectivity, and data storage. Contrarily, storing and operating information while maintaining its confidentiality and security throughout the whole operation process considering transparency in its privacy, data protection, and disaster recovery, is a concerning issue in the IoT. This talks about the vulnerabilities such as open telnet ports, which are devastating considering conditions such as outdated Linux firmware, communications of data without encryption, etc., being common in the IoT ecosystem,keeping it vulnerable to attacks such as a botnet, etc. [2]. To prevent this situation, research on false network detection respective to the concept of machine learning (ML) for tracking any irregular traffic in the IoT ecosystem needs to be prioritized, as the detection mainly deals with the behavior of those communications from the devices to the routers and switches through the existing network, and any irregular behavior can be traced down and operated on. IoT devices have become increasingly popular in recent years due to their ability to connect to the Internet and facilitate remote communication and control. However, the proliferation of these devices has also led to a rise in security breaches and attacks, particularly through the use of botnets [3]. Botnets are networks of compromised devices that are controlled remotely by a malicious actor. These devices can be utilized to initiate distributed denial of service (DDoS) attacks, send spam emails, or steal sensitive information. IoT devices, which often have weak security measures and are not regularly patched or updated, are particularly vulnerable to compromise and can be easily recruited into a botnet [4]. One example of a significant IoT-based botnet attack was the Mirai botnet, which was first identified in 2016. This botnet was responsible for several high-profile DDoS attacks, including an attack on the Krebs on Security website and an attack on the Dyn DNS provider, which disrupted Internet service for much of the Eastern United States. The Mirai botnet compromised many IoT devices, including routers, security cameras, and digital video recorders, by using a list of default or commonly used passwords to gain access. Other research has highlighted the security risks associated with IoT device interoperability and the use of third-party libraries. In a paper published in 2019, researchers found that the use of third-party libraries in IoT devices can lead to vulnerabilities that attackers can exploit. Additionally, the lack of standardization and security measures in developing IoT devices can lead to interoperability issues that attackers can exploit [5]. The ability to detect abnormal changes in a system's information, variables, or subsystems and identify it as a deviation from normal behavior is possible by thoroughly understanding the ecosystem and recognizing such changes based on prior experience. This concept can also be adapted to detecting normal and abnormal network traffic.