Author:
Giudici Paolo,Raffinetti Emanuela
Abstract
AbstractIn a world that is increasingly connected on-line, cyber risks become critical. Cyber risk management is very difficult, as cyber loss data are typically not disclosed. To mitigate the reputational risks associated with their disclosure, loss data may be collected in terms of ordered severity levels. However, to date, there are no risk models for ordinal cyber data. We fill the gap, proposing a rank-based statistical model aimed at predicting the severity levels of cyber risks. The application of our approach to a real-world case shows that the proposed models are, while statistically sound, simple to implement and interpret.
Funder
Università degli Studi di Milano
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Economics and Econometrics,Social Sciences (miscellaneous),Modeling and Simulation,Statistics and Probability,Analysis
Reference31 articles.
1. Afful-Dadzie, A., Allen, T.T.: Data-driven cyber-vulnerability maintenance policies. J. Qual. Technol. 46(3), 234–250 (2017)
2. Alexander, C.: Operational Risk: Regulation, Analysis and Management. Prentice Hall, New York (2003)
3. Brechmann, E., Czado, C., Paterlini, S.: Flexible dependence modeling of operational risk losses and its impact on total capital requirements. J. Bank. Finance 40, 271–285 (2014)
4. Cameron, A.C., Trivedi, P.K.: Regression Analysis of Count Data. Cambridge University Press, Cambridge (1998)
5. Cebula, J.J., Young, L.R.: A Taxonomy of Operational Cyber Security Risks. Technical Note, CMU/SEI-2010-TN-028, Software Engineering Institute, Carnegie Mellon University, pp. 1–34 (2010)
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献