## 2 Literature Review
### Internet of Things Machine Learning-Based IDPS
Consequently, the authors in [38] proposed a method that evaluated the specificity and complication of IoT security protection. They discovered that artificial intelligence (AI) approaches, like machine learning (ML), including ensemble classifiers, offer strong abilities and satisfy IoT security demands. The enhancement was associated with ensemble machine learning methods that used a variety of learning processes based on variable capacities. The authors improved predictability and decreased the likelihood of classification errors by combining the techniques used. The proposed method outcome ensured that the architecture enhanced the effectiveness of anomaly detection and intrusion prevention systems with high accuracy. The proposed methodology could have used large-scale IoT dataset deployments in the research investigation. The methodology did not utilize deep learning with IDPS capabilities for intrusion attack detection, including intrusion prevention methods. The authors in [39] proposed a survey method based on comparing and reviewing the latest deep learning-based approaches for intrusion detection. The authors also provided a comparative analysis of compatibility, challenges, feasibility, and real-time issues. The proposed method aimed to detect zero-day attacks. The proposed method could use precise intrusion detection and prevention methodology, and the process could be deployed in large-scale datasets, utilizing deep learning intrusion detection and prevention capability. Therefore, the authors in [40] proposed a method for analyzing the efficiency of deep learning techniques like CNN, LSTM, and XGBoost. Their research used a dual CNN approach, LSTM with three stacked layers, and XGBoost techniques. The method also utilized cross-validation to reduce overfitting by utilizing the IOBOTNET 2020 dataset. Based on the results, LSTM and XGBoost performed well and achieved fit scores with higher percentages. The proposed method dataset does not depend on large-scale IoT dataset deployment and IoT devices; the method could also use network topology. The proposed deep learning method could be precise in the research method. Furthermore, the authors in [41] proposed an intrusion detection system using the ROUT-4-2023 dataset. The dataset encompassed Black Hole, Flooding, DODAH Version Number, and Decreased Rank attacks. The proposed method in their study used statistical information graphs to investigate network traffic features encompassing all four attacks. The experiment testing the proposed method used machine learning models and deep learning architectures, comparing and analyzing confusion matrix outcomes and computational efficiency. In addition, the result of the proposed research method indicates that the Random Forest classifier achieved high accuracy. In addition, the dataset proposed in their research could use large-scale IoT device deployment capability and use IDPS network topology, and IDPS model capability could be proposed to be added to the method with the intrusion prevention system (IPS) method. Therefore, the authors in [42] proposed a hybrid intrusion detection system that used machine learning, file integrity monitoring, anomaly-based and signature-based rootkit detection, and signature-and-anomaly-based detection. The proposed research method used distributed architecture to share real-time threat intelligence amongst many nodes. The proposed method system offered proactive responses for automatic incident responses, endpoint quarantine, and improved detection capabilities. In addition, the proposed hybrid intrusion detection system combined rule-based and machine learning methods to detect and prevent attacks but did not use large-scale IoT datasets. The proposed method did not include network topology deployment with intrusion detection, prevention, and deep learning model capability. Ultimately, the authors in [43] proposed deep learning methods to analyze sensor data amounts in IoT devices to predict and detect patterns and to improve IoT system efficiency.