Securing Chemical Facilities Against Intentional Attacks: a Bayesian Network Approach for Asset Risk Assessment

Abstract

The consequences of a successful intentional attack to a process facility can be severe and could propagate among units generating the so-called domino effects, with potential effects on people, the environment, and assets. For these reasons, institutions and practitioners have found interest in determining the entity of damages associated with these events and integrating them into conventional safety and economic analyses. Still, approaches devoted to assessing the economic losses associated with intentional attacks have received less attention in the literature. This work presents a methodology to evaluate asset losses associated to intentional attacks. A probabilistic approach based on Bayesian Networks was adopted to include several factors associated to intrusion scenarios and their interdependencies. The developed methodology was then applied to a case study to demonstrate its potentialities. A demonstrational attack scenario was considered to test the methodology. The results show that a successful intentional attack might indeed lead not only to direct consequences on people, but also to relevant economical losses. Moreover, factoring in the synergistic performance of safety and security barriers allows to improve the estimation of asset losses.