Risk Management Using Cyber-Threat Information Sharing and Cyber-Insurance

Tosh, Deepak K.; Shetty, Sachin; Sengupta, Shamik; Kesan, Jay P.; Kamhoua, Charles A.

doi:10.1007/978-3-319-67540-4_14

Cited by 15 publications

(4 citation statements)

References 15 publications

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“…With respect to the social preferences, the utility function is U(x i , x −i |s), meaning that it also depends on the payoff of others x −i . Risk preferences, on the other hand, have been studied more by the researchers under the topics of loss aversion [50,51], insurance [52][53][54], willingness to pay [55][56][57], and endowment effect [58,59].…”

Section: Groupsmentioning

confidence: 99%

Systematically Understanding Cybersecurity Economics: A Survey

2021

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Insights in the field of cybersecurity economics empower decision makers to make informed decisions that improve their evaluation and management of situations that may lead to catastrophic consequences and threaten the sustainability of digital ecosystems. By drawing on these insights, cybersecurity practitioners have been able to respond to many complex problems that have emerged within the context of cybersecurity over the last two decades. The academic field of cybersecurity economics is highly interdisciplinary since it combines core findings and tools from disciplines such as sociology, psychology, law, political science, and computer science. This study aims to develop an extensive and consistent survey based on a literature review and publicly available reports. This review contributes by aggregating the available knowledge from 28 studies, out of a collection of 628 scholarly articles, to answer five specific research questions. The focus is how identified topics have been conceptualized and studied variously. This review shows that most of the cybersecurity economics models are transitioning from unrealistic, unverifiable, or highly simplified fundamental premises toward dynamic, stochastic, and generalizable models.

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Section: Groupsmentioning

confidence: 99%

Systematically Understanding Cybersecurity Economics: A Survey

2021

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“…For this reason, the last few years have seen an increase in the number of tools, predictive modeling and machine learning techniques applied to risk analysis 47,48 and cyber risk management [49][50][51] with the aim of predicting risk and contrasting cyber crimes. 52 An idea to improve the quality of prediction is to use a collaborative approach [53][54][55][56][57] with a Trusted Server where users store their security assessment information in order for it to be used to calculate the prediction.…”

Section: Problem Descriptionmentioning

confidence: 99%

Dare‐to‐Share: Collaborative privacy‐preserving recommendations with (almost) no crypto

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Bianchi

2021

Security and Privacy

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Collaborative recommending systems aim to predict a potential user‐item rating on the basis of remaining ones. Since, in several contexts, sharing of other users' ratings may be prevented by confidentiality concerns, several works have effectively addressed the design of privacy preserving recommenders. Still, most of the proposed solutions rely on advanced cryptographic methodologies, whose may conflict with the simplicity and viability requirements of real world deployments. In contrast, we propose an approach which does not require any complex cryptography. We show that whenever we can tolerate recommendations based on average values, we can transform the recommender into a privacy‐preserving one, by using two noncolluding replicas of the same system, and by distributing randomly “blinded” data to these replicas. To protecting each user's rating, a key asset of our approach is the ability to conceal which specific items are rated by which users. Our proposal is secure under the honest‐but‐curious attacker's assumption, and we show how it can be extended to guarantee robustness also against malicious adversaries. Finally, as a proof‐of‐concept, we present an implementation of the proposed approach for our motivating use case—collaborative assessment of computer/network vulnerabilities without revealing which of them affect one own infrastructure.

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“…In order to solve this problem, considering the choices of other users with similar systems, also through the use of new predictive algorithm, a solution is presented to the user. In the literature there are many examples of collaborative systems [25][26][27][28][29]. Our choice is different because in the software platform we implement algorithms able (i) to predict the user choices of risk values starting from what other users did before, (ii) understanding which users are more similar to the new one.…”

Section: Predictionmentioning

confidence: 99%

A Web Platform for Integrated Vulnerability Assessment and Cyber Risk Management

et al. 2019

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Cyber risk management is a very important problem for every company connected to the internet. Usually, risk management is done considering only Risk Analysis without connecting it with Vulnerability Assessment, using external and expensive tools. In this paper we present CYber Risk Vulnerability Management (CYRVM)-a custom-made software platform devised to simplify and improve automation and continuity in cyber security assessment. CYRVM's main novelties are the combination, in a single and easy-to-use Web-based software platform, of an online Vulnerability Assessment tool within a Risk Analysis framework following the NIST 800-30 Risk Management guidelines and the integration of predictive solutions able to suggest to the user the risk rating and classification.

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Risk Management Using Cyber-Threat Information Sharing and Cyber-Insurance

Cited by 15 publications

References 15 publications

Systematically Understanding Cybersecurity Economics: A Survey

Systematically Understanding Cybersecurity Economics: A Survey

Dare‐to‐Share: Collaborative privacy‐preserving recommendations with (almost) no crypto

A Web Platform for Integrated Vulnerability Assessment and Cyber Risk Management

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