## 6 Discussion
### Benefits, Limitations and Future Work  
This paper presented ideas on integrating methods for the anomaly detection and mitigation of different types (cyber-attacks, adversarial attacks, system failures) within the context of Industry 4.0. This is a step towards a holistic approach to dealing with anomalies in the FoF environment. The anomaly detection and mitigation approach from Figure 7 already comprises anomalies with dedicated sub-data, sub-detection and sub-mitigation methods. Additionally, more types of anomalies can be added to approach the complexity of Industry 4.0. In this regard, it is important that novel sub-methods are well integrated. The detected anomalies should be distinguishable to identify what needs to be mitigated. Furthermore, the mitigation sub-methods must fit into the decision plan so that the suitability of the individual methods for the respective situation can be extracted. Moreover, the presented methods for anomaly detection and mitigation alone exceed the state of the art. The application of the Cyber Kill Chain as a field-validated method to a new industrial use case of growing relevance regarding future factories introduces conceptually and operationally validated security considerations to the presented AGV fleet. The subsequent discussion of targeted cyber-threat scenarios has resulted in corresponding security measures tailored to AGVs and their proposed use-cases, thus providing a list of opportunities for detecting cyber-related anomalies and protecting AGVs from cyber-attacks. Since there is a limited amount of academic work dealing with the cyber-security of industrial AGVs in FoF, this contribution shows that security in future factories is indispensable and provides specific suggestions for detecting cyber-anomalies and addressing threats to AGVs with corresponding mitigations. For the detection of process anomalies, data on KPIs of AGVs are used to derive an adapted version of classical control charts for anomaly detection. Those charts consider the increasing dynamicity of FoFs by integrating dynamic mean values instead of static means, such as in the literature. With the non-static mean, it is possible to consider the predicted KPIs that could be received from a Digital Twin run as a prediction of dynamic behavior. The mitigation method of global reconfiguration provides a backup plan for dealing with anomalies if more specialized mitigation methods such as local reconfiguration do not provide sufficient mitigation solutions. This improves the resilience of a fleet of AGVs and helps to address the research gaps for resilient, sustainable material handling stressed in [61]. To the best of the authors' knowledge, this is the first study to address the subject of adversarial attacks in Industry 4.0. The two proposed mitigation methods should therefore provide initial guidance and raise awareness of this subject in the future. Figure 7: Anomaly detection and mitigation approach applied on the broken AGV example. The respective system (here, the fleet of AGVs) needs to be prepared properly to put the presented methodology for anomaly handling into practice. The first step is to decide which methods for anomaly detection and mitigation should actually be implemented. For example, when an already existing fleet of AGVs is equipped with the means to increase its resilience a posterior, it would require an unreasonable amount of effort to change the software architecture of the AGVs to a service-based architecture, and the implementation of the local reconfiguration for anomaly mitigation might be out of scope. Instead, other methods for increasing the redundancy of AGV components might be considered and integrated into the overall implementation. In the following, high-level requirements for implementing the presented methods are summarized.