A Dynamic Cybersecurity Risk Assessment Framework for SCADA-based Industrial Control Systems
Seyed Mehdi Hosseini
1
(
Department of Electrical and Computer Engineering, SR.C., Islamic Azad University, Tehran, Iran
)
Mohammad Ali Pourmina
2
(
Department of Electrical and Computer Engineering, SR.C., Islamic Azad University, Tehran, Iran
)
Ahmad Fakharian
3
(
)
Mehdi Khatir
4
(
Department of Electrical and Computer Engineering, SR.C., Islamic Azad University, Tehran, Iran
)
Keywords: Cybersecurity Risk Assessment, SCADA Security, Industrial Control Systems, Critical Infrastructure Protection, Machine Learning, Bayesian Networks,
Abstract :
constitute the backbone of critical infrastructure operations worldwide. The increasing convergence of Operational Technology (OT) with Information Technology (IT) networks has created unprecedented cybersecurity challenges, evidenced by sophisticated attacks including Stuxnet (2010), the Ukraine power grid incident (2015), and the Colonial Pipeline ransomware attack (2021). These systems demand exceptional reliability with 99.999% availability and sub-10ms latency for Programmable Logic Controllers. Conventional risk assessment methodologies exhibit significant qualitative limitations due to their static nature and inability to model dynamic threats, asset interdependencies, and temporal variations in operational context. This research introduces an advanced dynamic cybersecurity risk assessment framework specifically designed for SCADA systems in high-sensitivity industrial environments. The framework incorporates seven integrated components: layered asset valuation with damage impact weighting, Multilayer and Dynamic Bayesian Networks for probabilistic threat modeling, offline CVSS and ISO/IEC 18045 scoring mechanisms, decision-tree-based risk assessment aligned with IEC 62443 standards, secure state transition controllers, game-theoretic optimization for adaptive defense strategies, and a Random Forest-based mitigation recommender.
Through comprehensive MATLAB simulations, the framework demonstrates substantial improvements over static baselines, achieving a significant reduction in assessment uncertainty and an average risk score reduction of 28% across all assets, while maintaining false positive rates below 5%.
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