  Industrial Control Systems (ICSs) are widely used in critical infrastructures
that face various cyberattacks causing physical damage. With the increasing
integration of the ICSs and information technology (IT), ensuring the security
of ICSs is of paramount importance. In an ICS, cyberattacks exploit
vulnerabilities to compromise sensors and controllers, aiming to cause physical
damage. Maliciously accessing different components poses varying risks,
highlighting the importance of identifying high-risk cyberattacks. This aids in
designing effective detection schemes and mitigation strategies. This paper
proposes an optimization-based cyber-risk assessment framework that integrates
cyber and physical systems of ICSs. The framework models cyberattacks with
varying expertise and knowledge by 1) maximizing physical impact in terms of
failure time of the physical system, 2) quickly accessing the sensors and
controllers in the cyber system while exploiting limited vulnerabilities, 3)
avoiding detection in the physical system, and 4) complying with the cyber and
physical restrictions. These objectives enable us to jointly model the
interactions between the cyber and physical systems and study the critical
cyberattacks that cause the highest impact on the physical system under certain
resource constraints. Our framework serves as a tool to understand the
vulnerabilities of an ICS with a holistic consideration of cyber and physical
systems and their interactions and assess the risk of existing detection
schemes by generating the worst-case attack strategies. We illustrate and
verify the effectiveness of our proposed method in a numerical and a case
study. The results show that a worst-case strategic attacker causes almost 19%
further acceleration in the failure time of the physical system while remaining
undetected compared to a random attacker.
