为大型网络的网络保险定价
Pricing cyber insurance for a large-scale network
摘要：
针对网络保险损失数据缺乏的问题，提出了一种基于合成数据的大规模网络保险定价方法。合成数据由所提出的风险分散和恢复算法生成，该算法允许感染和恢复事件顺序发生，并且允许不同节点的随机等待时间对感染的依赖性。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。通过大量的模拟研究，了解了风险的分散和恢复机制，并揭示了最重要的承保风险因素。最后通过一个案例分析表明，所提出的方法和算法可以为网络保险定价提供参考。
英文摘要：
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.
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