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Cyber-physical systems (CPS), which integrate algorithmic control withphysical processes, often consist of physically distributed componentscommunicating over a network. A malfunctioning or compromised component in sucha CPS can lead to costly consequences, especially in the context of publicinfrastructure. In this short paper, we argue for the importance ofconstructing invariants (or models) of the physical behaviour exhibited by CPS,motivated by their applications to the control, monitoring, and attestation ofcomponents. To achieve this despite the inherent complexity of CPS, we proposea new technique for learning invariants that combines machine learning withideas from mutation testing. We present a preliminary study on a watertreatment system that suggests the efficacy of this approach, proposestrategies for establishing confidence in the correctness of invariants, thensummarise some research questions and the steps we are taking to investigatethem.

Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation