## 2 Literature Review  
### Internet of Things Machine Learning-Based IDPS  
In recent years, diverse contributions to IoT security have begun to exist. There have been diverse objectives in which the data produced are made possible using diverse methods and resources. We can review the features that govern existing datasets through many proposals. These proposals are available in the existing literature and are compared with the dataset features proposed by IDPS capability in this research. In [33], the authors proposed an artificial intelligence (AI)-based intrusion detection and prevention system (IDPS). The IDPS stops intrusions on IoT networks in real-time. The proposed research method used ensemble feature selection algorithms to address and eliminate the dataset's irrelevant and redundant features, improving the proposed model's performance. The dataset used in the study was the NBaIoT dataset, which is known for its high accuracy in intrusion detection. However, the NBaIoT dataset deployment was not on large-scale IoT devices. The NBaIoT dataset requires the use of deep learning multilayerperceptron (DLMLP). In addition, the model only trained processed network data at the IoT gateway, which predicted real-time attacks. In [34], the authors proposed a conjugate gradient-based improved GAN (CG-IGAN) anomaly detection system for IoT. The proposed CG-IGAN learned and collected reliable and practical features in unstable conditions. The CG-IGAN method uses a Botnet dataset, which is collected and extracted using Independent Component Analysis (ICA). The CG-IGAN method classifier used these attributes and improved the precision that identified malicious data. CG-IGAN improved the accuracy in identifying malicious data, contrasted with advanced IoT-based anomaly detection methods. The CG-IGAN method required a large-scale Botnet dataset. The CG-IGAN method required IDPS capability for the deep learning multilayer perceptron (DLMLP) method and network topology capability to capture and assess intrusion attacks. The authors in [35] proposed a method that used an intelligent system based on a particle swarm optimization algorithm to detect early attacks practically. The result shows that the proposed algorithm can detect the attack source. The proposed method is more bandwidth-consuming than traditional methods. The proposed method also supports intrusion prevention system functionality in next-generation firewalls. The proposed attack detection method could detect all modern IoT device attacks. The described method could include DLMLP and IDPS's capability to investigate intrusion attacks. Therefore, the authors in [36] proposed a method of IoT security that leveraged machine learning (ML) and deep learning (DL) techniques. The proposed method combined techniques to solve IoT security problems. In addition, the proposed method investigated how to enhance IoT security devices and applications that safeguard user data from unauthorized access and misuse. The proposed method could be deployed in a large-scale IoT dataset and could utilize the IDPS network topology available for evaluating the intrusions. In addition, the authors in [37] proposed a method that examined Snort's role as a robust intrusion detection tool in a network. Thus, the method in [37] proposed ways to prevent intrusion by thoroughly investigating security challenges in IoT network devices. In addition, the study focused on specific vulnerability detection and emphasized resource constraints, different communication protocols, and the need to tailor security measures for them. The paper thoroughly examined Snort intrusion detection architecture, and its application to develop custom rules for detecting intrusion attacks was studied. The method could use machine learning (ML) or deep learning techniques, including DLMLP and IDPS capability, to investigate intrusion attacks. Moreover, the proposed method could include a large-scale IoT dataset deployment solution and network topology to investigate intrusion attacks in their research.