This study aims to compare cyber insurance premiums with and without the frequency in a communication network effect. As a cybersecurity factor, the frequency in a communication network is influencing the speed of cyberattack transmission. It means that a network or a high activity node is more vulnerable than a network with low activity. Traditionally, cyber insurance pricing considers historical data to set premiums or rates. Conversely, the network security level can evaluate using the Monte Carlo simulation based on the epidemic model. This simulation requires spreading parameters, such as infection rate, recovery rate, and self-infection rate. Our idea is to modify the infection rate as a function of the frequency in a communication network. To generate the data, the node-based model uses probability distributions for the communication mechanism. It adopts the co-purchase network formation in market basket analysis for build weighted edges and nodes Simulations are used to compare the initial infection rate and the modified infection rate. In this paper, we considered prism and Petersen graph topology as case studies. The relative difference is a metric to compare the significance of premium adjustment. The results show that the premium for a node with a low level in a communication network can reach 28.28% lower than the initial premium. For a network, the premium can reach 20.99% lower than the initial network premium. Based on these results, insurance companies can adjust cyber insurance premiums based on computer usage to offer a more appropriate price
