Systemic risk modeling & evaluation through simulation & Bayesian networks

In the Risk Analysis domain an increasing interest has been gaining by the System Risk Analysis that aims at investigating the risk deriving by the interdependence of the system under consideration by other systems and, in general, by the interactions among them. Indeed, an adverse event occurring in a certain system can cause negative effects on the other interconnected systems and compromise their operation. An effective analysis of the Systemic Risk requires suitable methods and techniques able to handle the high level of complexity typical of Systems and Systems characterized by several interconnected, distributed, autonomous and changing components. In this context, the paper proposes a method for Systemic Risk Analysis that combines a Goal-Oriented Methodology for Requirement Modeling (GOReM) with a Model-Based method for System Dependability Analysis (RAMSoS). Such combination enables the modeling and the evaluation of Systemic Risk scenarios by using agent-based simulations and the complementary quantitative evaluation of performance indices through Bayesian Networks. A concrete exploitation of the proposed approach to Systemic Risk Analysis in the cyber-security domain is also presented.