A Robust Statistical Framework for Cyber‑Vulnerability Prioritisation Under Partial Information
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.