  Over the past decade, industrial control systems have experienced a massive
integration with information technologies. Industrial networks have undergone
numerous technical transformations to protect operational and production
processes, leading today to a new industrial revolution. Information Technology
tools are not able to guarantee confidentiality, integrity and availability in
the industrial domain, therefore it is of paramount importance to understand
the interaction of the physical components with the networks. For this reason,
usually, the industrial control systems are an example of Cyber-Physical
Systems (CPS). This paper aims to provide a tool for the detection of cyber
attacks in cyber-physical systems. This method is based on Machine Learning to
increase the security of the system. Through the analysis of the values assumed
by Machine Learning it is possible to evaluate the classification performance
of the three models. The model obtained using the training set, allows to
classify a sample of anomalous behavior and a sample that is related to normal
behavior. The attack identification is implemented in water tank system, and
the identification approach using Machine Learning aims to avoid dangerous
states, such as the overflow of a tank. The results are promising,
demonstrating its effectiveness.
