Proactive cyber-risk assessment is gaining importance due to its potential benefits in preventing cyber incidents across various sectors and addressing emerging vulnerabilities in cyber-physical systems. This study presents a robust statistical framework, using mid-quantile regression, to assess cyber vulnerabilities, rank them, and measure accuracy while dealing with partial knowledge. The model is tested with simulated and real data to support informed decision-making in operational scenarios.
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