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Proactive cyber-risk assessment is gaining momentum due to the wide range ofsectors that can benefit from the prevention of cyber-incidents. The increasingconnectivity of digital and (cyber-)physical systems requires more attention tocybersecurity to enhance the integrity, confidentiality, and availability ofdata. We introduce a statistical framework for the prioritisation ofcyber-vulnerabilities, using robust and interpretable regression models tosupport decision-making. Specifically, we take advantage of mid-quantileregression to deal with ordinal risk assessments, and we compare it to currentalternatives for cyber-risk ranking and graded responses, identifying a novelaccuracy measure suited for rankings with partial knowledge of existingvulnerabilities. Our model is tested on both simulated and real data fromselected databases that support the exploitation of cyber-vulnerabilities inreal contexts. The variety of information arising from such datasets allows usto compare multiple models based on their predictive performance, showing howaccessible information can influence perception and, hence, decision-making inoperational scenarios. Applications to threat intelligence are discussed too.

Cyber-risk Perception and Prioritization for Decision-Making and Threat Intelligence