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These days more companies are shifting towards using cloud environments toprovide their services to their client. While it is easy to set up a cloudenvironment, it is equally important to monitor the system's runtime behaviourand identify anomalous behaviours that occur during its operation. In recentyears, the utilisation of \ac{rnn} and \ac{dnn} to detect anomalies that mightoccur during runtime has been a trending approach. However, it is unclear howto explain the decisions made by these networks and how these networks shouldbe interpreted to understand the runtime behaviour that they model. On thecontrary, state machine models provide an easier manner to interpret andunderstand the behaviour that they model. In this work, we propose an approachthat learns state machine models to model the runtime behaviour of a cloudenvironment that runs multiple microservice applications. To the best of ourknowledge, this is the first work that tries to apply state machine models tomicroservice architectures. The state machine model is used to detect thedifferent types of attacks that we launch on the cloud environment. From ourexperiment results, our approach can detect the attacks very well, achieving abalanced accuracy of 99.2% and an F1 score of 0.982.

Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster