Modeling multivariate cybersecurity risks

作者：Peng, Chen; Xu, Maochao^*; Xu, Shouhuai; Hu, Taizhong

来源：Journal of Applied Statistics, 2018, 45(15): 2718-2740.

DOI：10.1080/02664763.2018.1436701

• Copula density forecast
• GARCH
• prediction
• vine copula
• Value-at-Risk

Modeling cybersecurity risks is an important, yet challenging, problem. In this paper, we initiate the study of modeling multivariate cybersecurity risks. We develop the first statistical approach, which is centered at a Copula-GARCH model that uses vine copulas to model the multivariate dependence exhibited by real-world cyber attack data. We find that ignoring the due multivariate dependence causes a severe underestimation of cybersecurity risks. Both simulation and empirical studies show that the proposed approach leads to accurate predictions of multivariate cybersecurity risks.

• 出版日期2018
• 单位中国科学技术大学