B0941
Title: Enhancing cyber risk assessment: Unfolding ordinal data models for effective analysis
Authors: Claudia Tarantola - University of Pavia (Italy) [presenting]
Silvia Facchinetti - Universita Cattolica Del Sacro Cuore Di Milano (Italy)
Maria Iannario - University of Naples Federico II (Italy)
Silvia Angela Osmetti - Università Cattolica di Milano (Italy)
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.
