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

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 he...

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Autores principales:	Yeftanus Antonio, Sapto Wahyu Indratno, Suhadi Wido Saputro
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6ff0a92b4e1a48d1b66fd8a50f10bad8
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spelling	oai:doaj.org-article:6ff0a92b4e1a48d1b66fd8a50f10bad82021-11-04T06:19:41ZPricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure1932-6203https://doaj.org/article/6ff0a92b4e1a48d1b66fd8a50f10bad82021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547698/?tool=EBIhttps://doaj.org/toc/1932-6203Cyber 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.Yeftanus AntonioSapto Wahyu IndratnoSuhadi Wido SaputroPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yeftanus Antonio Sapto Wahyu Indratno Suhadi Wido Saputro Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure
description	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.
format	article
author	Yeftanus Antonio Sapto Wahyu Indratno Suhadi Wido Saputro
author_facet	Yeftanus Antonio Sapto Wahyu Indratno Suhadi Wido Saputro
author_sort	Yeftanus Antonio
title	Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure
title_short	Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure
title_full	Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure
title_fullStr	Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure
title_full_unstemmed	Pricing of cyber insurance premiums using a Markov-based dynamic model with clustering structure
title_sort	pricing of cyber insurance premiums using a markov-based dynamic model with clustering structure
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6ff0a92b4e1a48d1b66fd8a50f10bad8
work_keys_str_mv	AT yeftanusantonio pricingofcyberinsurancepremiumsusingamarkovbaseddynamicmodelwithclusteringstructure AT saptowahyuindratno pricingofcyberinsurancepremiumsusingamarkovbaseddynamicmodelwithclusteringstructure AT suhadiwidosaputro pricingofcyberinsurancepremiumsusingamarkovbaseddynamicmodelwithclusteringstructure
_version_	1718445095961755648

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

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