# Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems
## 6 Conclusions  
This study explored the integration of IDS within DT for smart robotics, focusing on the effectiveness of machine learning algorithms in network intrusion detection. Using the CICIDS2017, 4 SICS 2015, and CIC Modbus datasets, we evaluated the performance of two classification models: RF and SVM. The results highlighted the advantages and limitations of each approach in detecting cybersecurity threats in industrial environments. In Table 19, the main insights derived from the analysis of the three datasets are presented. Given our results and reviewing other studies, such as that presented in [35], special attention is paid to the performance of the RF and SVM algorithms within the CICIDS2017 dataset context. The study observed that RF and SVM exhibited remarkable outcomes, with RF achieving an accuracy of approximately 99.37%, precision of 99.58%, recall of 99.37%, and an F1-score of 99.47%. Similarly, SVM also demonstrated competitive results, achieving an accuracy of 98.24%, precision of 98.40%, recall of 98.24%, and an F1-score of 98.31%. \begin{table}
\begin{tabular}{c c c c} \hline
**Confusion Matrix—SVM Validation** & **0** & **1** \\ \hline
0 & 788,639 & 87,921 \\
1 & 90,538 & 811,646 \\ \hline \end{tabular}
\end{table} 
Table 17: Confusion matrix—SVM for the test set from CIC Modbus 2023. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC-ROC** \\ \hline RF & 93\% & 93\% & 93\% & 0.93 & 0.93 \\ \hline SVM & 88\% & 88\% & 88\% & 0.88 & 0.88 \\ \hline \end{tabular}
\end{table} 
Table 18: Comparation between RF and SVM for the 4 SICS dataset. Figure 25: Classification report—SVM for the test and validation sets. These findings emphasize the efficacy of RF and SVM in detecting abnormal behaviors in network traffic, providing a robust basis for comparisons in the academic literature. This specific analysis contributes to the broader context of using machine learning algorithms in Digital Twin-based intrusion detection systems, highlighting the capability of these models to efficiently and accurately classify suspicious activities. For the other two datasets, the analysis is not as in-depth, which is one of the reasons why we decided to conduct this study. Analyzing the results above, we can conclude that the RF model performs exceptionally well for industrial traffic analysis, given its ability to balance detection efficiency and false-positive rates. In contrast, the SVM model exhibits lower precision, with a higher likelihood of generating false-positive alerts. To effectively utilize the SVM model, it is recommended to employ a hybrid architecture or rigorously fine-tune its parameters. The integration of Digital Twin-based intrusion detection systems (DT-IDSs) is crucial for cybersecurity in industrial environments, as it enables real-time monitoring, early threat detection, and system resilience against evolving cyber threats. Firstly, we have developed and implemented a model that integrates intrusion detection systems (IDSs) with Digital Twin (DT) technology. This model enables the simulation and assessment of the impact of cyber-attacks within a controlled environment, thereby facilitating proactive detection and rapid intervention in security incidents, minimizing the resulting losses and damages. Secondly, we conducted a comparative analysis between two popular algorithms, Random Forest and Support Vector Machine. This study demonstrated that RF is superior in the specific context of industrial network traffic due to its lower false alarm rate and enhanced accuracy. Additionally, in this work, we analyzed three complex and industrially relevant datasets (CICIDS2017, CIC Modbus, and 4 SICS 2015) to test and validate the proposed model. This not only demonstrates the versatility and robustness of our model but also provides a replicable methodology for testing future security solutions under real-world conditions.