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

Autor: Sapto Wahyu Indratno, Yeftanus Antonio, Suhadi Wido Saputro
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Computer and Information Sciences
Markov Models
Statistical methods
Computer science
Epidemiology
Science
Information Theory
Network topology
Research and Analysis Methods
Insurance Coverage
Insurance
Mathematical and Statistical Techniques
Clustering Coefficients
Econometrics
Cyber-Insurance
Medicine and Health Sciences
Cluster Analysis
Humans
Computer Networks
Cluster analysis
Risk management
Computer Security
Clustering coefficient
Risk Management
Multidisciplinary
Markov chain
business.industry
Statistics
Probability Theory
Markov Chains
Monte Carlo method
Graph Theory
Medical Risk Factors
Metric (mathematics)
Physical Sciences
Exponential Functions
Medicine
Engineering and Technology
business
Management Engineering
Mathematical Functions
Mathematics
Network Analysis
Network analysis
Research Article
Zdroj: PLoS ONE
PLoS ONE, Vol 16, Iss 10, p e0258867 (2021)
PLoS ONE, Vol 16, Iss 10 (2021)
ISSN: 1932-6203
Popis: 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.
Databáze: OpenAIRE
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