Over the last decades, the advancements in microelectronic technologies allowed for the embedding of complex digital sensors in several systems, ranging from home appliances to health tracking devices and industrial plant machinery. The resulting systems are, in general, quite complex, given the possible heterogeneity of their components and the non-trivial ways in which sensors may interact. In critical domains, formal methods have been employed to ensure the correct behaviour of a system. However, a complete specification of all the properties that have to be guaranteed turns out to be often out of reach, due to the inherent complexity of the system and of its interactions with the environment in which it operates. To overcome these limitations, some approaches that complement formal verification with model-based testing and monitoring have been recently proposed. In this paper, we argue for the opportunity of pairing monitoring with machine learning techniques in order to improve its ability of detecting critical system behaviours.
