Modeling multivariate cyber risks: deep learning dating extreme value theory

Mingyue Zhang Wu, Jinzhu Luo, Xing Fang, Maochao Xu and Peng Zhao

Journal of Applied Statistics, 2023, vol. 50, issue 3, 610-630

Abstract: Modeling cyber risks has been an important but challenging task in the domain of cyber security, which is mainly caused by the high dimensionality and heavy tails of risk patterns. Those obstacles have hindered the development of statistical modeling of the multivariate cyber risks. In this work, we propose a novel approach for modeling the multivariate cyber risks which relies on the deep learning and extreme value theory. The proposed model not only enjoys the high accurate point predictions via deep learning but also can provide the satisfactory high quantile predictions via extreme value theory. Both the simulation and empirical studies show that the proposed approach can model the multivariate cyber risks very well and provide satisfactory prediction performances.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:50:y:2023:i:3:p:610-630

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DOI: 10.1080/02664763.2021.1936468

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