A methodology for resilient safety-critical infrastructures using statistical model checking

Engineering safety-critical infrastructures for continuous operation is a challenging task. With increasingly integration and automation, these systems are overwhelmingly exposed to disruptions - arising from both accidental causes and malicious threats. In this paper, we report on our work on developing quan-titative risk assessment methods to ensue such infrastructures remain resilient to disruptions. Our approach involves building the quantitative domain models to carry: a) continuous risk analysis - estimating the likelihood of system disruption and quantify its damaging impact. b) identify the most crucial system components to keep the system operational. c) dictating optimal incident response strategies. Technically, we model the system in a compositional manner by building and composing stochastic timed automaton of each system component. Along with metrics of interest, these models are fed to the statistical model checker of UPPAAL SMC. Metrics of interest are disruption scenarios in the system model. A scenario is encoded in a variant of temporal logic for its use in the model checker. We use a realistic industrial case-study of steam-boiler system to showcase the methodology.