Abstract

SmartResilience project has provided a new methodology for assessing and managing resilience of critical infrastructures, such as energy and water supply, transportation networks and similar. The methodology is based on a continuously growing database of resilience indicators (currently over 5,000) allowing to quantitatively assess resilience of an infrastructure, thus quantifying its ability to cope with possible adverse scenarios/events, such as cyber-attacks, extreme weather of terrorist attacks, which alone or together can potentially lead to significant disruptions in its operation/functionality. Coping with these scenarios means preparing for them, being able to absorb/withstand their impacts, recover optimally from their impacts and adapt/transform to the continuously changing conditions. Application of the system in about 30 case histories so far, was initially envisaged as a mean of validating the methodology and the system, but with over 250 critical infrastructure related scenarios analyzed in the case histories, provide new possibilities for applying machine learning and other AI and BI methods. The paper proposes a method for MCDM-based optimization of investments (effort, time, finances) in measurable resilience improvement (resilience level), being in the focus of further development of the SmartResilience methodology and respective tools.