Semantic modeling of cyber threats in the energy sector using Dynamic Cognitive Maps and Bayesian Belief Network
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.
Библиографическая ссылка
Gaskova D.A., Massel A.G. Semantic modeling of cyber threats in the energy sector using Dynamic Cognitive Maps and Bayesian Belief Network // Proc.of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support. 2019. P.326-329. DOI: 10.2991/itids-19.2019.58
