Anomaly and intrusion detection in industrial cyber-physical systems has attracted a lot of attention in recent years. Deep learning techniques that require huge datasets are actively researched nowadays. The great challenge is that the real data on such systems, especially security-related data, is confidential, and a methodology for dataset generation is required. In this paper, the authors consider this challenge and introduce the methodology of dataset generation for research on the security of industrial water treatment facilities. The authors describe in detail two stages of the proposed methodology: the definition of a technological process and creating a testbed. The paper ends with a conclusion and future work prospects.