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Dynamic cyber risk estimation with competitive quantile autoregression
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Dzhamtyrova, Raisa and Maple, Carsten (2022) Dynamic cyber risk estimation with competitive quantile autoregression. Data Mining and Knowledge Discovery, 36 . pp. 513-536. doi:10.1007/s10618-021-00814-z ISSN 1384-5810.
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Official URL: http://dx.doi.org/10.1007/s10618-021-00814-z
Abstract
The increasing value of data held in enterprises makes it an attractive target to attackers. The increasing likelihood and impact of a cyber attack have highlighted the importance of effective cyber risk estimation. We propose two methods for modelling Value-at-Risk (VaR) which can be used for any time-series data. The first approach is based on Quantile Autoregression (QAR), which can estimate VaR for different quantiles, i. e. confidence levels. The second method, we term Competitive Quantile Autoregression (CQAR), dynamically re-estimates cyber risk as soon as new data becomes available. This method provides a theoretical guarantee that it asymptotically performs as well as any QAR at any time point in the future. We show that these methods can predict the size and inter-arrival time of cyber hacking breaches by running coverage tests. The proposed approaches allow to model a separate stochastic process for each significance level and therefore provide more flexibility compared to previously proposed techniques. We provide a fully reproducible code used for conducting the experiments.
Item Type: | Journal Article | ||||||||
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Subjects: | H Social Sciences > HD Industries. Land use. Labor Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Library of Congress Subject Headings (LCSH): | Computer security , Cyber intelligence (Computer security), Computer networks -- Security measures, Cyberspace -- Security measures, Risk management | ||||||||
Journal or Publication Title: | Data Mining and Knowledge Discovery | ||||||||
Publisher: | Springer | ||||||||
ISSN: | 1384-5810 | ||||||||
Official Date: | March 2022 | ||||||||
Dates: |
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Volume: | 36 | ||||||||
Page Range: | pp. 513-536 | ||||||||
DOI: | 10.1007/s10618-021-00814-z | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 21 February 2023 | ||||||||
Date of first compliant Open Access: | 21 February 2023 |
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