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.

网络风险建模一直是网络安全领域的一项重要但具有挑战性的任务，这主要是由风险模式的高维度和重尾造成的。这些障碍阻碍了多元网络风险统计模型的发展。在这项工作中，我们提出了一种基于深度学习和极值理论的多变量网络风险建模新方法。所提出的模型不仅可以通过深度学习进行高精度点预测，还可以通过极值理论提供令人满意的高分位数预测。仿真和实证研究均表明，所提出的方法可以很好地模拟多变量网络风险，并提供令人满意的预测性能。