Keywords: information security risk assessment, intelligent decision support system, cognitive modeling, scenario modeling, graph models
Intelligent decision support system for assessing information security risks of ICS
UDC 004.056
DOI: 10.26102/2310-6018/2023.43.4.029
The relevance of the article is due to the need to ensure information security of industrial control systems (ICS). Loss of control over industrial facilities can lead to undesirable consequences in a particular subject of the state or affect the economic indicators of the country as a whole as well as compromise the safety of the population. In this regard, this article aims to improve the procedure for quantitative assessment of information security risks as a necessary component of an integrated approach to ensuring information security, which helps to assess the feasibility of information security violation scenarios and identify their possible consequences for building an effective protection system. The architecture of a research prototype of an intelligent decision support system and a software implementation of tools for automating the modeling of attack scenarios and assessing the information security risks of ICS have been developed, the use of which makes it possible to increase the reliability and efficiency of information security risk assessment and, consequently, the choice of effective countermeasures at all stages of an industrial facility life cycle and its complex protection systems. The materials of the article are of practical value for information security specialists at all stages of the life cycle of distributed information and control systems of industrial facilities.
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Keywords: information security risk assessment, intelligent decision support system, cognitive modeling, scenario modeling, graph models
For citation: Kirillova A.D., Vulfin A.M., Vasilyev V.I., Guzairov M.B. Intelligent decision support system for assessing information security risks of ICS. Modeling, Optimization and Information Technology. 2023;11(4). URL: https://moitvivt.ru/ru/journal/pdf?id=1476 DOI: 10.26102/2310-6018/2023.43.4.029 .
Received 18.11.2023
Revised 01.12.2023
Accepted 20.12.2023
Published 31.12.2023