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The identification and mitigation of interdependencies among critical infrastructure elements such as telecommunications, energy and transportation are important steps in any protection strategy and are applicable in preventive and operative settings. This paper presents a graph-theoretical model and framework for analyzing dependencies based on a multigraph approach and discusses algorithms for automatically identifying critical dependencies. These algorithms are applied to dependency structures that simulate the scale-free structures found in many infrastructure networks as well as to networks augmented by random graphs.

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Abstract. Critical infrastructures are interconnected on multiple levels, and due to their size models with acceptable computational complexity and adequate modeling capacities must be developed. This paper presents the skeleton of a graph based model and sketches its capabilities.

The detection of attacks and intrusions based on anomalies is hampered by the limits of specificity underlying the detection techniques. However, in the case of many critical infrastructure systems, domain-specific knowledge and models can impose constraints that potentially reduce error rates. At the same time, attackers can use their knowledge of system behavior to mask their manipulations, causing adverse effects to observed only after a significant period of time. This paper describes elementary statistical techniques that can be applied to detect anomalies in critical infrastructure networks. A SCADA system employed in liquefied natural gas (LNG) production is used as a case study.