## 1 Introduction
Other security threats and challenges also focus on IoT system layers. The physical/perception layer is one of the IoT system device layers that can include significant security threats at the physical/perception layer. Therefore, the following essential security threats are worth discussing. Eavesdropping can occur in the physical layer. As a result, malicious intruder devices in IoT systems, including the end nodes, use passive sniffing traffic to obtain valuable information. Furthermore, hardware malfunctioning can occur in IoT system devices. Intelligent transportation systems (ITS), e-health, smart homes/cities, and the smart grid are affected. Consequently, hardware malfunctioning can lead to production faults, resulting in cyberattacks. There can be a significant impact not only on the system, but also, users' lives can be under threat [24]. In addition, we may use many of these IoT system smart devices to generate datasets. These datasets conform to many IoT applications but are prone to intrusions or cyberattacks [25]. Moreover, Malign data injection can affect IoT systems. Malign IoT can also counterfeit IoT devices and can be injected into IoT systems. It also can sniff the wireless traffic and introduce intrusion attacks. Intrusion attacks generally lead to the malfunctioning of the channel or system and produce fake messages, making the system unavailable for users. Furthermore, node cloning is also another IoT security challenge. As a result, there is no standardization, which leads to the forging of device duplication in IoT ecosystems [26]. Based upon this, insider intrusion attacks can swap legitimate devices with illegitimate devices, which happens during IoT system production in an operational state. Thus, node cloning attacks can lead to security parameter removal and firmware overwriting. Additionally, due to wireless vulnerability, unauthorized access to IoT devices can quickly occur. Moreover, there are many security vulnerabilities in default password usage, which can lead to built-in credentials by the intrusion attacker. These IoT system security vulnerabilities lead to design challenges for producing practical large-scale datasets with IoT security solutions and IDPS capability. The investigation of such a secure IoT solution with IDPS capability that includes IoT application for the CICIoT2023 dataset is currently unavailable in the literature. To investigate these secure IoT security challenges based on the literature, researchers have proposed machine learning (ML) methods based on the literature. For instance, researchers [27; 28; 29; 30] have proposed IoT security in distributed IoT devices without any robust security mechanism. They only used intrusion detection in IoT devices without intrusion prevention mechanisms. In addition, they did not include any clear network topology for investigating intrusion attacks in large-scale datasets. Moreover, other researchers, including [31; 32], have tried to provide IoT security solutions in their strategies for solving intrusion attacks. Researchers [32] used large-scale datasets without IDPS capability, whilst researchers [31] used deep learning (DL) multilayer perceptron (MLP); however, they did not consider extensive large-scale IoT device topology. Consequently, these researchers did not succeed in investigating and preventing intrusion attacks because of the limitations of the methods used for intrusion attacks and prevention proposed in the literature.