论文标题
大规模网络的定价网络保险
Pricing cyber insurance for a large-scale network
论文作者
论文摘要
面对缺乏网络保险损失数据，我们提出了一种基于合成数据的大规模网络定价网络保险的创新方法。合成数据是由提出的风险扩散和恢复算法生成的，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解释随机大规模网络的拓扑不确定性。进行了广泛的模拟研究，以了解风险扩散和恢复机制，并发现最重要的承保风险因素。还提出了一个案例研究，以证明拟议的方法和算法可以相应地调整以提供网络保险定价的参考。
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.
