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Stochastic Dependencies Between Critical Infrastructures

Authors:
Sandra König
Stefan Rass

Keywords: critical infrastructure; stochastic dependencies; Markov chain; risk propagation

Abstract:
Critical infrastructures (CIs) are characterized by their high importance for the welfare of a society and failure of such an infrastructure has a significant impact on our everyday life. However, a problem in one critical infrastructure also affects other infrastructures, e.g., if electricity is only partly available this also affects hospitals. The effects of even a partial failure of a provider on a critical infrastructures are hard to predict unless strict assumptions are made. The damage depends, among other things, on the availability of substitutes, but also on external influences such as weather, temporary demand or load peaks, etc., which is why we propose a stochastic model where the state of an infrastructure is a random variable. Each infrastructure changes its state depending on what the other CIs do, based on a probabilistic change transition regime. This allows to model complex interdependencies, whose underlying dynamics may be stochastic or deterministic yet partly unknown. The model of the entire CI thus consists of several Markov chains, which retains simplicity for implementation in a software such as R, and flexibil- ity to capture various forms of mutual influence between CIs. We illustrate this by giving a small example. The main contribution of this work is a model that partly unifies three different models of risk propagation (Bayesian networks, percolation and system dynamics) under a single simulation/percolation framework.

Pages: 106 to 110

Copyright: Copyright (c) IARIA, 2017

Publication date: September 10, 2017

Published in: conference

ISSN: 2162-2116

ISBN: 978-1-61208-582-1

Location: Rome, Italy

Dates: from September 10, 2017 to September 14, 2017