To address the lack of cyber insurance loss data, we propose an innovative approach to pricing cyber insurance for a large-scale network using synthetic data. The synthetic data is generated by the proposed risk-spreading and risk-recovering algorithm. The algorithm allows the sequential occurrence of infection and recovery events, and it allows the dependence of the random waiting time to infection for different nodes. The scale-free network framework is adopted to account for the uncertain topology of the random large-scale network. Extensive simulation studies are conducted to understand the risk-spreading and risk-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 to provide reference for cyber-insurance pricing.
This paper was funded by the 2017 Individual Grants Competition funded by the Society of Actuaries and the Casualty Actuarial Society.
