  Modeling cyber risks has been an important but challenging task in the domain
of cyber security. It is mainly because of 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 prediction via extreme value theory. The simulation
study shows that the proposed model can model the multivariate cyber risks very
well and provide satisfactory prediction performances. The empirical evidence
based on real honeypot attack data also shows that the proposed model has very
satisfactory prediction performances.
