Stefan Schauer, Stefan Rass, & Sandra König. (2021). Simulation-driven Risk Model for Interdependent Critical Infrastructures. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 404–415). Blacksburg, VA (USA): Virginia Tech.
Abstract: Critical infrastructures (CIs) in urban areas or municipalities have evolved into strongly interdependent and highly complex networks. To assess risks in this sophisticated environment, classical risk management approaches require extensions to reflect those interdependencies and include the consequences of cascading effects into the assessment. In this paper, we present a concept for a risk model specifically tailored to those requirements of interdependent CIs. We will show how the interdependencies can be reflected in the risk model in a generic way such that the dependencies among CIs on different levels of abstraction can be described. Furthermore, we will highlight how the simulation of cascading effects can be directly integrated to consistently represent the assessment of those effects in the risk model. In this way, the model supports municipalities' decision makers in improving their risk and resilience management of the CIs under their administration.
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Stefan Schauer, Stefan Rass, Sandra König, Thomas Grafenauer, & Martin Latzenhofer. (2018). Analyzing Cascading Effects among Critical Infrastructures. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 428–437). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: In this article, we present a novel approach, which allows not only to identify potential cascading effects within a network of interrelated critical infrastructures but also supports the assessment of these cascading effects. Based on percolation theory and Markov chains, our method models the interdependencies among various infrastructures and evaluates the possible consequences if an infrastructure has to reduce its capacity or is failing completely, by simulating the effects over time. Additionally, our approach is designed to take the intrinsic uncertainty into account, which resides in the description of potential consequences a failing critical infrastructure might cause, by using probabilistic state transitions. In this way, not only the critical infrastructure's risk and security managers are able to evaluate the consequences of an incident anywhere in the network but also the emergency services can use this information to improve their operation in case of a crisis and anticipate potential trouble spots.
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