Abstract
The development of information and communication technology not only has positive impacts, but also negative impacts, especially in the cybersecurity sector. Insurance companies need to create a relatively new insurance product, namely cybersecurity insurance. However, development of cybersecurity insurance still needs further investigation because there is no standard actuarial table like mortality table in life insurance. This article will discuss the modeling of infection and recovery process of a node and various other connected nodes in a computer network of the company using non-Markov model in the case of absence of dependence between cybersecurity risks, applying the Monte Carlo simulation method to obtain experimental data with various distributions – Weibull, Lognormal, and Inverse Gaussian – for the calculation of premium charged by insurance companies to insured companies interested in purchasing cybersecurity insurance products. Standard deviation premium principle and exponential utility premium principle are used to calculate premium. We concluded that the infection and recovery time with a long-tailed distribution has a lower premium price compared to those with a short-tailed distribution.
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