  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.
