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The paper considers the violation of cybersecurity as a possibility of a real impact (intentional or accidental) from cyberspace on the physical infrastructure of a digital energy facility. In energy security studies, such impacts are considered as extreme situations, including critical and emergency situations. A model of scenarios of extreme situations in the energy sector caused by cyber threats using Bayesian Belief Network and the stages of modeling are considered in more detail. The five main stages are i) modeling cyber threats vectors of intrusion and advance towards the target asset; ii) modeling an attack on a target system in the technological segment of the local area network; iii) modeling technogenic threats to energy security caused by cyber threats; iv) modeling consequences at the level of the facility system; v) modeling consequences at the level of the infrastructure. This approach allows one to build cause and effect relationships from vulnerabilities in the cyber environment to the consequences. Modeling stages are aimed at increasing the level of cyber situational awareness, which, in turn, related with energy security issues.

The article discusses the use of semantic modeling in the analysis of threats to energy security (ES). Semantic modeling is proposed to be applied at a qualitative level, followed by quantitative assessment of the ES level in studies of energy security. Exercise of traditional software systems provides a quantitative assessment, which is characterized by the duration of information preparation, and the formation and adjustment of large enough models for computational experiments. At the first level, a decision maker selects options for which a detailed rationale is required based on the results of semantic modeling. These options are calculated at the second level. The article presents basic notions of Dynamic Cognitive Maps (DCM) and Bayesian Belief Network (BBN). The paper presents the information model that is suggested to use in analyzing cyber threats in the energy sector. Exemplification of the impact of cyber threats on an energy facility carried out under Dynamic Cognitive Maps and Bayesian Belief Network is given in this article. The advantages of each tool and their role in analyzing cyber threats in the energy sector are presented.

В статье рассматриваются  результаты исследования ситуационной осведомленности в киберсреде энергетических объектов. Для повышения информированности о состоянии киберсреды таких объектов предлагаются модель сценариев экстремальных ситуаций в энергетике на основе байесовской сети доверия и численный метод определения киберситуационной осведомленности. Модель основана на причинно-следственных связях между уязвимостями локальной вычислительной сети (ЛВС) и возможных киберугрозах, представленных в виде векторов проникновения в ЛВС,  векторов развития кибернетических атак и векторов атак на целевой актив, объединённых в сценарии. В работе ставится акцент на сценарии, последствия которых могут расцениваться как экстремальные ситуации в энергетике, вызванные киберугрозами. The article describes the research direction of situational awareness in the cyber environment on energy facilities. A model based on a Bayesian Belief Network and a numerical method for determining cyber situational awareness are proposed to increase awareness of the cyber environment state on such facilities. The model is based on causal relationships between the vulnerabilities of the local area network and possible cyber threats, presented in the form of vectors of penetration into the network, vectors of the development of cyber attacks and attack vectors on the target asset, combined in scenarios. The work focuses on scenarios, the consequences of which can be regarded as extreme situations in the energy sector caused by cyber threats.