## 8 Conclusions and Future Work
The DL, thanks to its deep learning capabilities, can automatically extract high-level and complex features from the raw data. This helps identify patterns and hidden correlations that could be indicative of malicious behavior or suspicious activity within computer networks. Cyberattacks are becoming more sophisticated, using advanced techniques such as signature evasion and camouflage of malicious activity. DL can address this challenge by allowing models to automatically learn new attack patterns without the need for explicit rules. DL models can recognize patterns of suspicious behavior even when attacks take never-before-seen forms, making network defenses more robust and adaptable. Network security requires analyzing massive amounts of data from various traffic streams, system logs, and other sources. DL excels at processing large datasets, enabling rapid and simultaneous analysis of multiple data sources. This can lead to better detection of threats and faster processing of information, allowing you to respond quickly to cyber attacks. DL can play a crucial role in detecting anomalous activity in computer networks. For example, DL models can learn the normal behaviors of network devices and individual users, allowing you to detect deviant behavior that could indicate malicious activity. This ability to spot anomalies and unusual behavior helps identify intrusions before they cause significant damage. DL also offers opportunities to improve the performance of existing machine learning models. Deep neural networks can be used to further refine existing ML models, enabling more accurate predictions and reducing the number of false positives and false negatives in threat detections. While this article focuses on machine learning, it is clear that future developments in computer network security will increasingly require the use of deep learning. Extracting complex features, detecting sophisticated attacks, analyzing large amounts of data, detecting anomalous activity and improving model performance are just some of the potential benefits offered by the DL to improve the security of computer networks. The integration of DL into the network security space promises to provide a more robust and resilient defense against increasingly complex and sophisticated cyberthreats [61; 62; 63; 64; 65; 66; 67; 68]. Future developments of this research focus on the application of ML/AI models in industrial mechatronics. Examples include ensuring the safety of on-board control systems in assisted and autonomous driving vehicles, and developing advanced anomaly detection systems for information traffic in industrial automation systems, such as drive control systems for manipulator robots or trajectory planning systems for robots [69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82]. Authors has equally contributed at this article. All authors have read and agreed to the published version of the manuscript. This research is partially funded by the Horizon Europe program under grant agreement 101092850 (AERO project) and by the European High-Performance Computing Joint Undertaking (JU) program under grant agreement 101033975 (EUPEX). Not applicable. The authors declare no conflict of interest.