Structural models for fog computing based internet of things architectures with insurance and risk management applications

Zhang, Xiaoyu; Xu, Maochao; Su, Jianxi; Zhao, Peng

doi:10.1016/j.ejor.2022.07.033

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…Aggregate loss. The aggregate loss can be expressed using the loss resulting from a single attack by conditioning on the number of attacks, which is a critical component in the frequency-severity approach as the industry standard for pricing insurance risks [19,44,46]. Similarly, the loss resulting from a single attack can be expressed using the local cost c by conditioning on the cluster size S. Using also that the number of attacks is Poisson distributed, we obtain the following result.…”

Section: Resultsmentioning

confidence: 97%

Exact Insurance Premiums for Cyber Risk of Small and Medium-Sized Enterprises

Chiaradonna¹,

Lanchier²

2022

Math. Model. Nat. Phenom.

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As cyber attacks have become more frequent, cyber insurance premiums have increased, resulting in the need for better modeling of cyber risk. Jevtic and Lanchier[20] proposed a dynamic structural model of aggregate loss distribution for cyber risk of small-and-medium-sized enterprises under the assumption of a tree-based local-area-network topology that consists of the combination of a Poisson process, homogeneous random trees, bond percolation processes, and cost topology. Their model assumes that the contagion spreads through the edges of the network with the same fixed probability in both directions, thus overlooking a dynamic cyber security environment implemented in most networks, and their results give an exact expression for the mean of the aggregate loss but only a rough upper bound for the variance. In this paper, we consider a bidirectional version of their percolation model in which the contagion spreads through the edges of the network with a certain probability moving toward the lower level assets of the network but with another probability moving toward the higher level assets of the network. Also, our different mathematical approach leads to exact expressions for both the mean and the variance of the aggregate loss, and therefore an exact expression for the insurance premiums.

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Section: Resultsmentioning

confidence: 97%

Exact Insurance Premiums for Cyber Risk of Small and Medium-Sized Enterprises

Chiaradonna¹,

Lanchier²

2022

Math. Model. Nat. Phenom.

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“…10.11113/mjfas.v19n3.2906 One could say that Fog computing is the foundation upon which the cloud rests. The following is a summary of the features that define Fog computing, as described in [31,38] :…”

Section: Fog Computing Characteristicsmentioning

confidence: 99%

Design a Crime Detection System based Fog Computing and IoT

Waheed,

Sakran,

Mohd Rahim

et al. 2023

Mal. J. Fund. Appl. Sci.

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The Internet of Things (IoT) is a cutting-edge innovation that facilitates the cost-effective development of smart system architectures. Although current regulations necessitate installing an analog fire alarm system, such a system lacks the intelligence to instantly notify the appropriate parties in a timely fashion. In addition, since people are not always present, an analog fire alarm will not be able to prevent immediate danger or damage in the event of a fire. Therefore, the incident must be reported as soon as possible to the appropriate party in order to lessen the impact of a fire. In this study, we suggest a smart fire-alarm system made of a fire sensor and a sound sensor that can both detect fire and noise as well as the status of the analog fire alarm system to ascertain whether the analog fire alarm system is operational. We first tested our proposed smart fire alarm system to determine its effectiveness before putting it into use. From there, we ran experiments to determine how well it worked. The outcomes show that the system is trustworthy in a range of scenarios.

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“…Thus, managing cyber risk comprises many forms such as the utilization of cybersecurity protection for vulnerability mitigation (Bokan & Santos, 2021;Ranganayaki et al, 2021), understanding cyber risk losses on a network (see, e.g., Acemoglu et al, 2016;Santos et al, 2007), cyber-insurance for minimizing the consequential financial risk (Federal Financial Institutions Examination Council, 2018; X. Zhang et al, 2023), and the use of risk management frameworks for risk identification and assessment (Meszaros & Buchalcevova, 2017;Millar, 2016;Paté-Cornell et al, 2018;Rios Insua et al, 2021). Cybersecurity protection strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…And so, according to The Institute of Risk Management (2018), cyber risk is defined as “any risk of financial loss, disruption or damage to the reputation of an organization from some sort of failure of its information technology systems” (The Institute of Risk Management, 2018). Thus, managing cyber risk comprises many forms such as the utilization of cybersecurity protection for vulnerability mitigation (Bokan & Santos, 2021; Ranganayaki et al., 2021), understanding cyber risk losses on a network (see, e.g., Acemoglu et al., 2016; Santos et al., 2007), cyber‐insurance for minimizing the consequential financial risk (Federal Financial Institutions Examination Council, 2018; X. Zhang et al., 2023), and the use of risk management frameworks for risk identification and assessment (Meszaros & Buchalcevova, 2017; Millar, 2016; Paté‐Cornell et al., 2018; Rios Insua et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach

2023

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Networks like those of healthcare infrastructure have been a primary target of cyberattacks for over a decade. From just a single cyberattack, a healthcare facility would expect to see millions of dollars in losses from legal fines, business interruption, and loss of revenue. As more medical devices become interconnected, more cyber vulnerabilities emerge, resulting in more potential exploitation that may disrupt patient care and give rise to catastrophic financial losses. In this paper, we propose a structural model of an aggregate loss distribution across multiple cyberattacks on a prototypical hospital network. Modeled as a mixed random graph, the hospital network consists of various patient‐monitoring devices and medical imaging equipment as random nodes to account for the variable occupancy of patient rooms and availability of imaging equipment that are connected by bidirectional edges to fixed hospital and radiological information systems. Our framework accounts for the documented cyber vulnerabilities of a hospital's trusted internal network of its major medical assets. To our knowledge, there exist no other models of an aggregate loss distribution for cyber risk in this setting. We contextualize the problem in the probabilistic graph‐theoretical framework using a percolation model and combinatorial techniques to compute the mean and variance of the loss distribution for a mixed random network with associated random costs that can be useful for healthcare administrators and cybersecurity professionals to improve cybersecurity management strategies. By characterizing this distribution, we allow for the further utility of pricing cyber risk.

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Structural models for fog computing based internet of things architectures with insurance and risk management applications

Cited by 6 publications

References 30 publications

Exact Insurance Premiums for Cyber Risk of Small and Medium-Sized Enterprises

Exact Insurance Premiums for Cyber Risk of Small and Medium-Sized Enterprises

Design a Crime Detection System based Fog Computing and IoT

Framework for cyber risk loss distribution of hospital infrastructure: Bond percolation on mixed random graphs approach

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