Understanding the behaviour and risks of Critical Infrastructure (CI) requires an understanding of the dependencies, and consequently Interdependencies, within and between critical infrastructures. Knowing the existence and possible mitigating policies against the effects of interdependencies can be used to increase the dependability and resilience of critical infrastructure. The Preliminary Interdependency Analysis (PIA) is a methodology for performing relatively quick interdependency analysis in critical infrastructures. Also, PIA-based models can be used to support the design and implementation of Middleware Improvement Technology (MIT). The steps required in using PIA are:
- Scenario statment. The scenario defines, to some extent, the scope of the analysis (how much of the real world should be included in the analysis), the operational contexts of, and relevant behaviour for, the infrastructures to which PIA is to be applied.
- Service model definition. This process requires an understanding of the processes by which services are delivered, the relevant time-scales and financial boundaries for the services. Stages in defining the service model may be cyclical.
- Dependency identification. Various forms of dependencies between service model concepts are defined.
- Interdependency identification. This is based on the dependencies identified in the previous stage.
- Probabilistic model definition and refinement, based on service model. The probabilistic model is used, to analyse the consequences of interdependencies identified in the previous stage.
- Probabilistic model analysis. After validating the model the modeller may use the probabilistic model to reason about the consequences (and possible mitigating strategies against them) of the existence of interdependencies. The transition rates that do not alter the model’s behaviour significantly when their values are varied within pre-specified, reasoned ranges indicate weak dependencies in the model. The transitions modelled by these rates need not be worried about and focus can be turned to the remaining transitions, testing the extent to which the model’s behaviour is dependent on the ranges of these transitions.
- The overall output can be used to shape high fidelity and other models
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