The criticality of desalination plants, which greatly rely on Industrial Control
Systems (ICS), has heightened due to the scarcity of clean water. This reliance
greatly emphasizes the necessity of securing these systems, alongside implementing a robust risk assessment protocol. To address these challenges and the existing limitations in prevalent risk assessment methodologies, this thesis proposes a risk assessment approach for ICS within desalination facilities. The proposed strategy integrates Bayesian Networks (BNs) and Dynamic Programming (DP). The thesis develops BNs into multilevel Bayesian Networks (MBNs), a form that effectively handles system complexity, aids inference, and dynamically modifies risk profiles.
These networks account for the interactions and dynamic behaviors of system components,providing a level of responsiveness often missing in traditional methods. A standout feature of this approach is its consideration of the potential attackers’perspective, often neglected but critical for a comprehensive risk assessment and the development of solid defense strategies. DP supplements this approach by simplifying complex problems and and identifying the most optimal paths for potential attacks. Therefore, this thesis contributes greatly to enhancing the safety of critical infrastructures like water desalination plants, addressing key deficiencies in existing safety precautions.
|Date of Award||Jul 9 2023|
|Original language||English (US)|
- Computer, Electrical and Mathematical Sciences and Engineering
|Supervisor||Charalambos Konstantinou (Supervisor)|
- Bayesian Network
- Risk Assessment
- Water Desalination