面对缺乏网络保险损失数据的情况，我们提出了一种基于合成数据的大型网络网络保险定价方法的创新方法。通过建议的风险分散和恢复算法生成综合数据，该算法允许感染和恢复事件顺序发生，并允许随机等待时间依赖于不同节点的感染。采用无标度网络框架来考虑随机大规模网络的拓扑不确定性。我们进行了大量的模拟研究，以了解风险的扩散和恢复机制，并揭示最重要的承保风险因素。还提供了一个案例研究，以证明所提出的方法和算法可以进行相应的调整，以为网络保险定价提供参考。
原文题目：Pricing cyber insurance for a large-scale network
原文：Facing the lack of cyber insurance loss data, we propose an innovative approach for pricing cyber insurance for a large-scale network based on synthetic data. The synthetic data is generated by the proposed risk spreading and recovering algorithm that allows infection and recovery events to occur sequentially, and allows dependence of random waiting time to infection for different nodes. The scale-free network framework is adopted to account for the topology uncertainty of the random large-scale network. Extensive simulation studies are conducted to understand the risk spreading and recovering mechanism, and to uncover the most important underwriting risk factors. A case study is also presented to demonstrate that the proposed approach and algorithm can be adapted accordingly to provide reference for cyber insurance pricing.
原文作者：Lei Hua, Maochao Xu
原文地址：https://arxiv.org/abs/2007.00454
大型网络的网络保险定价（CS SI）.pdf