## 8 Conclusions and Future Work  
This paper demonstrates the excellent performance achieved by machine learning (ML) models in detecting attacks, specifically in the domains of Anomaly Detection (binary classification) and Anomaly Classification (multi-class problems). To provide a comprehensive and relevant analysis, the study compares the performance of these models using three widely used datasets in the literature. The paper presents the entire operational flow of dataset management and manipulation, highlighting the techniques employed to optimize model performance. It discusses the advantages and disadvantages of different approaches to dataset manipulation, feature selection, and reduction. The evaluation of classification metrics commonly used in such problems has been performed, aiming to provide a comprehensive comparison of the models' performance. The results indicate that Decision Trees (DT) and Random Forests (RF) consistently outperform other models across all selected datasets, for both binary and multi-class classification. Moreover, the paper includes an analysis of the computational complexity, specifically computation time, of the models. It demonstrates that DT exhibits the lowest computational burden among the models evaluated. The scope of this research extends to the evaluation of ML models for embedded data traffic safety applications in mechatronic systems, such as fully electrified vehicles, complex industrial automation systems, and industrial robotic systems. Additionally, the study explores the potential of deep learning models, highlighting their effectiveness in various tasks. Integrating deep learning models into intrusion detection systems has the potential to improve accuracy and handle complex data, thereby reducing false alarms, enhancing detection rates, and enabling real-time monitoring for anomaly \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dataset** & **“Best” ML Models** & **Dataset Management** & **Performance** & **Most Relevant References** \\ \hline KDD99 (only binary) & RF, DT and KNN & \begin{tabular}{c} DT and KNN perform \\ better with preliminary feature transformation, reduction and scaling, RF require less \\ preliminary dataset \\ manipulation. \\ \end{tabular} & \begin{tabular}{c} Comparable \\ performance with very \\ high accuracy (\(>\)99\%) \\ and very low FPR (\(<\)0.5\%) \\ \end{tabular} & [23, 38, 41] \\ \hline UNSW-NB15 (both binary and multiclass) & RF and ANN & \begin{tabular}{c} RF require feature \\ reduction. ANN perform \\ also without preliminary \\ manipulation. \\ \end{tabular} & \begin{tabular}{c} Comparable \\ low FPR in binary \\ classification. \\ RF perform better for \\ multiclass IDS with \\ medium accuracy \\ (\(<\)85\%) and low FPR \\ \end{tabular} & [30, 31] \\ \hline CSE-CIC-IDS 2018 (both binary and multiclass) & DT, RF and KNN & \
\begin{tabular}{c} Required complete \\ dataset manipulation \\ Workflow. \\ \end{tabular} &
\begin{tabular}{c} Comparable \\ high accuracy and very \\ low FPR in binary \\ classification. \\ RF perform better for \\ multiclass IDS with \\ medium-high accuracy \\ (\(>\)90\%) and low FPR \\ \end{tabular} & [19, 41] \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Summary of most relevant works. detection--particularly crucial for high-traffic networks. The main objective of this review article is to examine the state of the art of machine learning (ML) in the area of computer network security, but it is important to emphasize that future developments in this area will increasingly focus on applying deep learning (DL). DL offers enormous potential to address complex and unknown challenges in detecting cyber threats and improving network security. Here are some reasons why DL will be a key pillar for future development in computer network security. One of the main challenges in the field of network security is the processing of large amounts of data from different sources.