## 1 Introduction
Typically, to ensure the development of security analytical solutions for IDPS in real-time secure data provision scenarios, this research resolves the gap left by previous methods such that the data produced in the IoT devices should (i) include an IDPS IoT network dataset with robust intrusion detection and prevention mechanisms in place, being able to detect and prevent any threats, thereby mitigating any new form of intrusion attacks that may harm IoT operations; (ii) be obtained from large-scale, extensive, real-time IDPS IoT network device topology, enabling the detection and prevention of any new or different intrusions; and (iii) detect and prevent any new intrusion attacks that have a malicious intention to pervade in IoT network devices. Consequently, this study's main objective includes proposing a novel intrusion detection and prevention system (IDPS) intrusion attack system model. The model investigates intrusion attacks in large-scale datasets. The proposed research method uses the DLMIDPS model for intrusion detection and prevention system models. Using the dataset, our pro posed DLMIDPS model investigates intrusion attacks in a full large-scale IoT dataset. The proposed method in this research promotes, develops, designs, and implements a new IoT security analytical application solution. It also provides real-time IoT network device IDPS operations capability. We have implemented 33 intrusion attacks on IoT network devices based on this. The implementation of our DLMIDPS model utilizes IDPS topology, which comprises about 105 devices with intrusion attacks. We classify the intrusion attacks into six categories based on benign, DDoS (distributed denial of service), DoS (denial of service), MITM (man in the middle attack), Marai, and Recon. Moreover, malicious IoT network devices include attack categories such as web-based, brute force, and spoofing. Data processing has appropriately screened off these categories. Therefore, the intrusion attacks are excluded from the trained dataset by the intrusion detection and prevention system. The processed data are prepared and ready for use in several formats. We use feature extraction in our evaluation and engineer new dataset features. The research contributions involve the following:  
* We develop, design, and implement the DLMIDPSM. The DLMIDPSM evaluates intrusion attacks in the topology of IoT devices based on our proposed topology in this research, IDPST. * We develop, design, and implement the intrusion detection and prevention system topology (IDPST). IDPST analyzes intrusion detection systems (IDS) and intrusion prevention systems (IPS). * We propose a large-scale real-time CICIoT2023 dataset with IDPS capability. * We implement, document, obtain, and analyze data based on 33 intrusion attacks categorized into six classes against the IoT devices dataset. * We develop, design, implement, and evaluate the deep learning multilayer perceptron intrusion detection and prevention system (DLMIDPSM) and machine learning (ML). The combined model is known as ML and DLMIDPSM. We use this combined model to classify, detect, and prevent IoT network device traffic that represents benign or malicious attacks. * We develop performance metrics using the precision, accuracy, F1-score, and confusion matrix to assess the performance of the proposed solution. The research is organized as follows: Section 2 is the literature review; Section 3 is the proposed solution and the methodology; Section 4 is the training, validating/testing of the dataset utilizing the DLMIDPSM model; Section 5 is the experimental simulation setup and analysis; and Section 6 is the conclusion.