| Abstrakt: |
Successful cyber attacks against industrial systems, such as water treatment systems, can lead to irreparable consequences for public health and the economy. Machine learning and deep learning could help detecting and forecasting previously unknown cyber attacks but require specific datasets. The number of publicly available datasets in this field is very limited and the majority of the publicly available datasets used in cyber security tasks have severe flows. In this paper, the authors introduce the unified methodology for the generation of the dataset for industrial water treatment security. Detailed specification of stages of the methodology is given. The paper ends with a usage scenario describing preparatory stages for dataset generation for the cybersecurity research in water treatment systems, namely, specification of the technological process, testbed development, and development of the attack model for the considered technological process. The developed methodology will be used for the dataset generation, that, in turn, will be used to develop and test cyber attack detection methods based on machine learning and deep learning, and to strengthen the water treatment systems’ security. |
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