Modeling multivariate cyber risks: deep learning dating extreme value theory

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.

Keywords:

• Cyber attacks
• GPD
• heavy tail
• high-dimensional dependence
• LSTM

Acknowledgments

The authors are grateful to the AE and the anonymous referee for their insightful and constructive comments, which guided them in revising and improving the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Peng Zhao was supported in part by the National Natural Science Foundation of China under Grant 11871252, and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.