Cyber Threat Risk Assessment in Energy Based on Cyber Situational Awareness Techniques
The high rate of digitalization into all spheres of human life including the energy sector, on the one hand, increases the efficiency of the physical infrastructure management at facilities and simplifies the interaction between system users, and on the one hand, it incites risks associated with interaction in the cyber environment. Both aspects referred to the relevance of this research. In this regard, it’s imperative to utilize artificial intelligence methods and technologies to assess cyber threat risk in the energy sector. It’s connected with the tendency of systems to increase the sophistication and the increase of the amount of information that is important in assessing risks in the cyber environment. The article proposes a method and model for assessing the risks of the cyber threat in the energy sector, developed by the authors. The proposed method differs by the integration of semantic modeling methods, expert systems technology, and cognitive graphics. Semantic methods serve for analyzing the cyber threats’ impact on energy facilities account for energy security for current research. Such methods offer efficiencies in the absence or incompleteness of data in modeling the behavior of systems that defies formal description or sufficiently accurate prediction. The kind of semantic modeling type as Bayesian Belief Network serves to probabilistic risk assessment at an energy facility. The graph’s nodes are interrelated events corresponding to the vulnerabilities of the assets of the local area network, cyber threats, technogenic threats to energy security, and the consequences of their implementation. The probabilistic component of risk is understood as the possibility of a meaningful effect (intentional or accidental) on the physical infrastructure of a digital energy facility from cyberspace in this context. That sort of effect is considered an extreme situation, including critical and emergency situations. The risk assessment is based on the posterior probabilities of the nodes corresponding to the consequences are used, as well as components of damages, taking into account the possibility of an extreme situation. The feasibility of methods and models is illustrated with an example at the end of the article. © 2022, Springer Nature Switzerland AG.
Библиографическая ссылка
Gaskova D., Galperova E., Massel A. Cyber Threat Risk Assessment in Energy Based on Cyber Situational Awareness Techniques // Communications in Computer and Information Science. Vol.1539 CCIS. 2022. P.134-145. ISBN (print): 9783030954932. DOI: 10.1007/978-3-030-95494-9_11
Скопировать
