Cyber Insurance Rate Making Based on Markov Model for Regular Networks Topology

Abstract

Computers and Internet play a key role in the processes of data transaction, data exchange and data storage in a business. Malicious software or computer viruses is one of the biggest threats that can attack computer networks and cause huge losses due to loss of data and information. As a way to transfer risk, cyber insurance requires precise and appropriate calculations even though many challenges are faced including the effects of differences in network structure. Standards of cyber insurance that have not been established as in the mortality table for life insurance open the possibility to set a standard calculation based on network structure by determining cyber insurance rates. This study uses a general susceptible-infectious-susceptible model with Markovian property to simulate the process of spreading computer virus and calculate the total loss for each computer. Rate making will consider the number of infected neighbours on a node as an exposure to set insurance rates on regular network topology. The simulation process shows that the rates at each node are affected by the probability of initial infection, the degree of each node on the network, and the parameters of infection or recovery.

Adjusting cyber insurance premiums based on frequency in a communication network

“…However, both still assumed that each computer (or node) is identical. We have demonstrated similar outcomes on regular networks [13]. Under the same parameter settings, each node with the same degree provided equal premiums.…”

Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure

“…This measure incorporates both degrees and the local clustering coefficient. Thus, nodes with the same degree do not always have the same premium as those in Table 2 or prior studies by Xu and Hua (2019) [ 19 ] and Antonio and Indratno (2021) [ 26 ].…”

“…It can be seen from the upper bound of each node that coincides with each other. By studying regular graphs, Antonio and Indratno (2021) [ 26 ] support the substantial effect of degrees on the model. Other factors that impact the rate of infection have not been explored in this model.…”

“…However, experimental results by Xu and Hua [ 19 ] only showed the strong influence of the degree of a node in a network on cyber losses and premiums. To confirm this, we conducted a study on the regular graph using the Markov model and obtained similar results [ 26 ]. The degree of a node can only explain the number of neighbors but has not described the relationship between neighbors.…”

“…This element has been effectively integrated into the premium calculation. By giving a more realistic premium based on the clustering structure, this suggested technique can improve the Markov-based model developed by Xu and Hua (2019) [ 19 ] and Antonio and Indratno (2021) [ 26 ]. Thus, the flexibility of the proposed approach in application enables it to provide premium improvements that are not homogenous (overestimate) and are more suitable.…”