Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 323–332 (2017)
Abstract. In this paper, we present the Bayesian Attack Graph for Smart Grid (BAGS) tool to quantify smart grid resilience in the presence of multiple cyber-physical attacks. BAGS takes system functions, network architecture, applications and a vulnerability report as input and generates three Bayesian Networks at three different levels of hierarchy. The top level network is called Functional Bayesian Network that defines how smart grid functions are connected. System engineers can select a particular function on a dashboard and view the Network Bayesian Network of that function at the second level. They can also choose a particular network component to see the list of vulnerabilities and the probability of associated compromise at the third level. System engineers can incorporate this functionality into their system and analyze the impact of any compromised component of the smart grid system on its resilience. Furthermore, BAGS helps to identify the failure paths in advance from one power grid function to another so that they can devise secure strategies and deploy resources effectively and efficiently.
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