English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Industrial Control Systems (ICSs) are widely used in critical infrastructuresthat face various cyberattacks causing physical damage. With the increasingintegration of the ICSs and information technology (IT), ensuring the securityof ICSs is of paramount importance. In an ICS, cyberattacks exploitvulnerabilities to compromise sensors and controllers, aiming to cause physicaldamage. Maliciously accessing different components poses varying risks,highlighting the importance of identifying high-risk cyberattacks. This aids indesigning effective detection schemes and mitigation strategies. This paperproposes an optimization-based cyber-risk assessment framework that integratescyber and physical systems of ICSs. The framework models cyberattacks withvarying expertise and knowledge by 1) maximizing physical impact in terms offailure time of the physical system, 2) quickly accessing the sensors andcontrollers in the cyber system while exploiting limited vulnerabilities, 3)avoiding detection in the physical system, and 4) complying with the cyber andphysical restrictions. These objectives enable us to jointly model theinteractions between the cyber and physical systems and study the criticalcyberattacks that cause the highest impact on the physical system under certainresource constraints. Our framework serves as a tool to understand thevulnerabilities of an ICS with a holistic consideration of cyber and physicalsystems and their interactions and assess the risk of existing detectionschemes by generating the worst-case attack strategies. We illustrate andverify the effectiveness of our proposed method in a numerical and a casestudy. The results show that a worst-case strategic attacker causes almost 19%further acceleration in the failure time of the physical system while remainingundetected compared to a random attacker.

An Integrated Cyber-Physical Risk Assessment Framework for Worst-Case Attacks in Industrial Control Systems