Method for monitoring the state of elements of cyber-physical systems based on time series analysis

The wide spread of cyber-physical systems, as well as the widespread integration of computing resources into physical entities, have led to an increase in the risks of deliberate and accidental security incidents. In this regard, the development of new methods and tools and improvement of the existing ones for monitoring such systems is of particular relevance. The methods being created and modernized should have increased recall and precision of identification, especially for critical infrastructure objects. An original method for processing data for monitoring the state of cyber-physical systems based on time series analysis using significance weights as a post-processing of classification results was proposed. The method differs from the existing ones by the combined approach that combines the use events of information security and functional safety in monitoring systems. It is characterized by the use of an ensemble of decision trees as well as parallel classifiers and Fishburn weight coefficients in the analysis of the set of the most informative features obtained from time series. The applicability of the method was substantiated by conducting of a computational experiment on a known data set which characterizes the functioning of the information and physical components in the implementation of various types of attacks on the components of the experimental stand of the cyber-physical water treatment system. When using the developed method, the identification precision increased by 1.45 % compared to the best approaches presented in other scientific works, and the recall increased by 4.45 % and amounted to 99.85 % for both indicators. The results obtained are adapted for practical use in systems for identifying the state of cyber-physical systems. The theoretical significance lies in the possibility of using the results of the study in the design of systems for monitoring the information security and functional safety of cyber-physical systems.