Abstract
Cyber insurance is a risk management option to cover financial losses caused by cyberattacks. Researchers have focused their attention on cyber insurance during the last decade. One of the primary issues related to cyber insurance is estimating the premium. The effect of network topology has been heavily explored in the previous three years in cyber risk modeling. However, none of the approaches has assessed the influence of clustering structures. Numerous earlier investigations have indicated that internal links within a cluster reduce transmission speed or efficacy. As a result, the clustering coefficient metric becomes crucial in understanding the effectiveness of viral transmission. We provide a modified Markov-based dynamic model in this paper that incorporates the influence of the clustering structure on calculating cyber insurance premiums. The objective is to create less expensive and less homogenous premiums by combining criteria other than degrees. This research proposes a novel method for calculating premiums that gives a competitive market price. We integrated the epidemic inhibition function into the Markov-based model by considering three functions: quadratic, linear, and exponential. Theoretical and numerical evaluations of regular networks suggested that premiums were more realistic than premiums without clustering. Validation on a real network showed a significant improvement in premiums compared to premiums without the clustering structure component despite some variations. Furthermore, the three functions demonstrated very high correlations between the premium, the total inhibition function of neighbors, and the speed of the inhibition function. Thus, the proposed method can provide application flexibility by adapting to specific company requirements and network configurations.
Highlights
Cyber risk management using cyber insurance is increasingly needed
The results showed that the network topology was an essential element for pricing cyber insurance contracts and cyber risk management
We looked at the linear relationship between the total clustering coefficient function (TN) and the upper bound (UB) to prove this assertion
Summary
Cyber risk management using cyber insurance is increasingly needed. Cyber risk is a type of operational risk that arises from the execution of cyberspace activities, posing a threat. Assuming that social networks have a high community structure and clustering coefficient, Wu and Liu [32] proposed a new model to study their influence on epidemics According to their findings, the degree of the community determines the spread of epidemics in community networks. This study proposes a Markov-based model with the network structure effect, namely, the ε-SIS model with a clustering coefficient factor for cyber insurance pricing. We propose a modified Markov-based algorithm with different rates at the individual-level ε-SIS model to generate synthetic cyber-attack data in this study. This algorithm is a modification of the individual-level SIS process algorithm with homogeneous rates.
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