In today's increasingly digitalized world, where organizations face the constant impact of technological advancements, the proliferation of cyber attacks poses a significant threat across various industries. While quantitative loss data is often scarce, experts in the field can provide a qualitative assessment of cyber attack severity on an ordinal scale. To analyze cyber risk effectively, it is natural to employ order response models. These models allow for exploring how experts assess the severity of cyberattacks based on a range of explanatory variables that describe the attack’s characteristics. Additionally, a measure of the diffusion of attack effects is incorporated through a network structure into the model’s explanatory variables. Apart from describing the methodology behind these models, a comprehensive analysis of a real dataset is presented. This dataset includes information on serious cyber attacks that have occurred worldwide, offering valuable insights into the practical application of the approach. By unravelling the complexities of cyber risk assessment and leveraging ordinal data models, the aim is to empower organizations to better understand and mitigate the potential impact of cyberattacks

Tarantola, C., Facchinetti, S., Osmetti, S. A., Iannario, M., Enhancing cyber risk assessment: Unfolding ordinal data models for effective analysis, in Ana Colubi, E. J. K. A. M. D. (ed.), PROGRAMME AND ABSTRACTS,16th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2023) and 17th International Conference on Computational and Financial Econometrics (CFE 2023), ECOSTA ECONOMETRICS AND STATISTICS, Berlino 2023: 155- 155 [https://hdl.handle.net/10807/263035]

Enhancing cyber risk assessment: Unfolding ordinal data models for effective analysis

Facchinetti, Silvia;Osmetti, Silvia Angela;
2023

Abstract

In today's increasingly digitalized world, where organizations face the constant impact of technological advancements, the proliferation of cyber attacks poses a significant threat across various industries. While quantitative loss data is often scarce, experts in the field can provide a qualitative assessment of cyber attack severity on an ordinal scale. To analyze cyber risk effectively, it is natural to employ order response models. These models allow for exploring how experts assess the severity of cyberattacks based on a range of explanatory variables that describe the attack’s characteristics. Additionally, a measure of the diffusion of attack effects is incorporated through a network structure into the model’s explanatory variables. Apart from describing the methodology behind these models, a comprehensive analysis of a real dataset is presented. This dataset includes information on serious cyber attacks that have occurred worldwide, offering valuable insights into the practical application of the approach. By unravelling the complexities of cyber risk assessment and leveraging ordinal data models, the aim is to empower organizations to better understand and mitigate the potential impact of cyberattacks
2023
Inglese
PROGRAMME AND ABSTRACTS,16th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2023) and 17th International Conference on Computational and Financial Econometrics (CFE 2023)
978-9925-7812-7-0
ECOSTA ECONOMETRICS AND STATISTICS
Tarantola, C., Facchinetti, S., Osmetti, S. A., Iannario, M., Enhancing cyber risk assessment: Unfolding ordinal data models for effective analysis, in Ana Colubi, E. J. K. A. M. D. (ed.), PROGRAMME AND ABSTRACTS,16th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2023) and 17th International Conference on Computational and Financial Econometrics (CFE 2023), ECOSTA ECONOMETRICS AND STATISTICS, Berlino 2023: 155- 155 [https://hdl.handle.net/10807/263035]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/263035
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