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.
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.
Cyber insurance ratemaking (CIRM) is a procedure used to set rates (or prices) for cyber insurance products provided by insurance companies. Rate estimation is a critical issue for cyber insurance products. This problem arises because of the unavailability of actuarial data and the uncertainty of normative standards of cyber risk. Most cyber risk analyses do not consider the connection between Information Communication and Technology (ICT) sources. Recently, a cyber risk model was developed that considered the network structure. However, the analysis of this model remains limited to an unweighted network. To address this issue, we propose using a graph mining approach (GMA) to CIRM, which can be applied to obtain fair and competitive prices based on weighted network characteristics. This study differs from previous studies in that it adds the GMA to CIRM and uses communication models to explain the frequency of communications as weights in the network. We used the heterogeneous generalized susceptible-infectious-susceptible model to accommodate different infection rates. Our approach adds up to the existing method because it considers the communication frequency and GMA in CIRM. This approach results in heterogeneous premiums. Additionally, GMA can choose more active communications to reflect high communications contribution in the premiums or rates. This contribution is not found when the infection rates are the same. Based on our experimental results, it is apparent that this method can produce more reasonable and competitive prices than other methods. The prices obtained with GMA and communication factors are lower than those obtained without GMA and communication factors.
The main purposes of this study is to asses on comparison of parameter estimation methods of the Pareto distribution. The estimation methods include moment, maximum likelihood estimation, probabilityweightedmoment, and generalizedmoment methods. Based on unbiasness, variance, and consistency properties, the results demonstrate that in estimating parameters of the Pareto distribution, the maximum likelihood method is the one of the best estimation methods.
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