## 6 Conclusions  
With the proliferation of many IoT datasets, including the CICIoT2023 dataset, urgent security solutions are essential for protecting IoT devices. Such a security solution requires urgent attention to redesign the dataset of IoT devices. We utilize the CICIoT2023 dataset and provide it with an IDPS capability, using an intrusion detection system (IDS) and an intrusion prevention system (IPS) model capability proposed in this research as the DLMIDPSM. Our proposed DLMIDPSM detects and mitigates all intrusion attacks that arise nowadays in IoT devices. In our proposed research method, we utilize the proposed deep learning multilayer perceptron (DLMLP) [31] and deploy it with a large-scale CICIoT2023 dataset, optimized for IDPS. DLMLP provides IDPS capability. Therefore, we deploy DLMLP with IDPS capability in our proposed research method, the DLMIDPSM. We use the DLMIDPSM to offer an efficient intrusion detection and prevention system (IDPS). Furthermore, our proposed IDPST model can utilize machine learning (ML) and DLMLP models, which are combined as ML and DLMIDPSMs, for efficiently capturing all new intrusion attacks that emerge in the IoT devices in CICIoT2023 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline BenignTraffic & 0.00 & 0.00 & 0.00 & 2489 \\ DDoS & 0.82 & 0.99 & 0.90 & 76,498 \\ DoS & 0.88 & 0.39 & 0.54 & 18,053 \\ MITM & 0.00 & 0.00 & 0.00 & 706 \\ Mirai & 0.99 & 0.62 & 0.77 & 5838 \\ Recon & 0.71 & 0.25 & 0.37 & 703 \\ accuracy & & 0.85 & 104,287 & \\ macro avg & 0.57 & 0.38 & 0.43 & 104,287 \\ weighted avg & 0.81 & 0.83 & 0.80 & 104,287 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation figure values about precision/accuracy and f1-score results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Precision** & **Decision Tree [31] MLP** & **SVM [31] MLP** &
\begin{tabular}{c} **ML and DLMIDPSMs** \\ **Including [31,32]** \\ \end{tabular} \\ \hline Mirai & 74.63 & 83.06 & 99.0\% \\ \hline DoS & 74.57 & 82.84 & 88.0\% \\ \hline DDoS & 73.9 & 83.31 & 82.0\% \\ \hline Recon & 74.66 & 83.56 & 71.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Values of precision of intrusion attack detection based on the combined model method used.  
Furthermore, ML and DLMIDPSMs are also used in our proposed research method to redesign a new IoT device dataset, such as the CICIoT2023 dataset, providing IDPS capability, which we investigate in our proposed IDPST. ML and DLMIDPSMs detect and prevent new intrusions in IoT device datasets such as the CICIoT2023. In addition, our proposed DLMIDPSM also predicts all intrusion attacks with an accuracy of over 85 percent, compared to that generated in the method in [31], which had an accuracy of 81 percent. The combined machine learning model, the deep learning ML model, and the DLMIDPSMs are efficient and have a significant precision/accuracy of 99% in detecting Mirai attacks compared to the model in method [31], which used decision tree (DT) and support vector machine (SVM) and had a precision and accuracy of 74% and 83%, respectively, when detecting Mirai attacks, depicting a trustworthy intrusion source.  
In the future, we would like to explore and build on our proposed methods and depend more on ANN, CNN, RNN, and other DLMLP models to investigate IoT device dataset security solutions. We aim to analyze performance metrics like accuracy and precision and F1-score, which confirm the legitimacy of our proposed IoT security method solution.  
This research received no external funding at this time.  
Data Availability StatementData are available upon request.  
The author acknowledges that he is the sole contributor to this research.  
The author declares no conflicts of interest.