## 6 Discussion
### Benefits, Limitations and Future Work
Therefore, the presented security considerations require corresponding standards, procedures and guidelines to address the human factor with regard to AGV security. This topic constitutes an aspect for future work to explore. When using AI-related methods, we do not need to build a simulator, namely Digital Twins; only the collected data from the real world are required. However, collecting as much clean data as possible is also a challenge because more data can give us more accurate results. This issue needs to be addressed soon. As shown in Figure 6, data exchange is a crucial element in anomaly processing, not only between operational systems but also between components of anomaly detection and mitigation. A data lake is one possibility to provide all systems and components with the required data whenever needed. This prevents the need to store the same data multiple times in various components. In this paper, it was not investigated how the full potential of data lakes can be used for storing and providing unstructured data, as well as the benefits of a growing number of anomaly detection and mitigation methods, which will be considered as future work. From a cyber-security perspective, it is difficult to identify malicious intent within the technically valid commands an attacker issues to an AGV. As long as these commands are syntactically valid and controlled variables remain within defined constraints, malicious commands are indistinguishable from legitimate ones. However, historical operational process data could be leveraged to address this shortcoming of traditional threat detection that relies solely on syntax and static variable boundaries. Production systems supported by AGVs usually follow a strictly defined process that requires specific commands to be issued at corresponding process steps. This implies that the order and often also the parameters of commands are highly predictable, and one can expect recurring patterns. Therefore, past process data contain baselines of regular process flows that could be used to train an AI system to detect malicious anomalies, even if they were triggered by technically legitimate commands. As sketched in Section 5.2.2, predictions on transportation tasks were already used to feed a Digital Twin of the fleet for predicting other AGV parameters. It remains open to check how the combination of predictions with Digital Twins for detecting anomalies in the presented use-case performs in comparison with purely AI-based methods. To account for naturally occurring process deviations, this AI would need to be trained on large data sets to contain representative normal process behavior. It would ideally keep learning while in operation to acquire new process behavior. As computational resources become widely available in interconnected factories, historical process data provide significant opportunities for effective anomaly detection in future production systems. Conceptualization, M.G. and L.F.; software, C.B. ; writing--original draft preparation, L.F., E.K., A.S., A.W., C.-Y. K., B.W., M.G., E.M. and P.B. ; writing--review and editing, A.S. and L.F.; visualization, A.W., C.-Y.K. and P.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the German Federal Ministry of Education and Research grant number 01 |S18061C. Not applicable. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.