<![CDATA[Industrial Control Systems play a critical role in modern industrial infrastructures, including manufacturing, energy, transportation, and critical utilities. The increasing integration of operational technology with information technology has significantly expanded the attack surface of these systems, making cybersecurity risk assessment an essential component of industrial resilience. This study aims to analyze and synthesize existing cybersecurity risk assessment approaches for Industrial Control Systems by examining quantitative, qualitative, and hybrid methods reported in recent literature. The research adopts a structured literature-based analytical method, focusing on models such as Bayesian networks, game theory, fuzzy logic, optimization-based frameworks, and vulnerability scoring systems. The results indicate that dynamic and asset-based risk assessment models provide more accurate and context-aware risk estimations compared to static approaches. Furthermore, integrating cyber and physical impact analysis enhances the capability to prioritize critical assets and predict worst-case attack scenarios. The findings contribute to a comprehensive understanding of current risk assessment methodologies and highlight key challenges related to data availability, model scalability, and real-time applicability. This study concludes that future cybersecurity risk assessment frameworks for Industrial Control Systems should emphasize dynamic modeling, cyber-physical integration, and adaptive evaluation mechanisms to address evolving threats effectively.]]>
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