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Mean-field-game model for botnet defense in cyber-security
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Kolokoltsov, V. N. (Vasiliĭ Nikitich) and Bensoussan, A. (2016) Mean-field-game model for botnet defense in cyber-security. Applied Mathematics & Optimization, 74 (3). pp. 669-692. doi:10.1007/s00245-016-9389-6 ISSN 0095-4616.
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Official URL: http://dx.doi.org/10.1007/s00245-016-9389-6
Abstract
We initiate the analysis of the response of computer owners to various offers of defence systems against a cyber-hacker (for instance, a botnet attack), as a stochastic game of a large number of interacting agents. We introduce a simple mean-field game that models their behavior. It takes into account both the random process of the propagation of the infection (controlled by the botner herder) and the decision making process of customers. Its stationary version turns out to be exactly solvable (but not at all trivial) under an additional natural assumption that the execution time of the decisions of the customers (say, switch on or out the defence system) is much faster that the infection rates.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QC Physics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Library of Congress Subject Headings (LCSH): | Mean field theory , Computer networks -- Security measures, Phase transformations (Statistical physics) | ||||||
Journal or Publication Title: | Applied Mathematics & Optimization | ||||||
Publisher: | Springer New York | ||||||
ISSN: | 0095-4616 | ||||||
Official Date: | December 2016 | ||||||
Dates: |
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Volume: | 74 | ||||||
Number: | 3 | ||||||
Page Range: | pp. 669-692 | ||||||
DOI: | 10.1007/s00245-016-9389-6 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 15 January 2021 | ||||||
Date of first compliant Open Access: | 18 January 2021 |
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