Infrastructures are becoming more and more complex, and mutually dependent, according to many dimensions, such as geographic proximity, resource exchange and cyber dependencies.
The paradox is that, very often, critical infrastructures interact in ways that are hidden or not well understood by the single infrastructures’ owners, while these interactions lead to catastrophic cascading failures and domino effects.
This is the reason why sector-specific monitoring and predictor systems, although being very sophisticated, fail to capture the behaviour of the infrastructures in critical situations.
Having this in mind, FACIES aims to define cooperation strategies for automatic detection of failures and attacks. However, the solution has to be achieved in a decentralized and peer-to-peer perspective where only partial and not sensible data are shared among the different subjects.
The main question addressed by FACIES is how to identify, in the early stage a failure and/or attack in a scenario composed by several and interdependent critical infrastructures.
Specifically the project aims to illustrate the feasibility of a distributed approach able to detect in the early stage failures and malicious adverse events. Such a solution is based on a system of system modelling and use partial and incomplete information. Notice that this is a crucial point due to the sensible nature of many data and the consequent reluctance of the operators to share such a data.