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Over the past decade, industrial control systems have experienced a massiveintegration with information technologies. Industrial networks have undergonenumerous technical transformations to protect operational and productionprocesses, leading today to a new industrial revolution. Information Technologytools are not able to guarantee confidentiality, integrity and availability inthe industrial domain, therefore it is of paramount importance to understandthe interaction of the physical components with the networks. For this reason,usually, the industrial control systems are an example of Cyber-PhysicalSystems (CPS). This paper aims to provide a tool for the detection of cyberattacks in cyber-physical systems. This method is based on Machine Learning toincrease the security of the system. Through the analysis of the values assumedby Machine Learning it is possible to evaluate the classification performanceof the three models. The model obtained using the training set, allows toclassify a sample of anomalous behavior and a sample that is related to normalbehavior. The attack identification is implemented in water tank system, andthe identification approach using Machine Learning aims to avoid dangerousstates, such as the overflow of a tank. The results are promising,demonstrating its effectiveness.

Anomaly-Based Intrusion Detection System for Cyber-Physical System Security