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The use of partially observable Markov decision processes to optimally implement moving target defense

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dc.contributor.author Mcabee, Ashley
dc.contributor.author Tummala, Murali
dc.contributor.author Mceachen, John
dc.date.accessioned 2020-12-24T20:27:27Z
dc.date.available 2020-12-24T20:27:27Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/71461
dc.description.abstract For moving target defense (MTD) to shift advantage away from cyber attackers, we need techniques which render systems unpredictable but still manageable. We formulate a partially observable Markov decision process (POMDP) which facilitates optimized MTD capable of thwarting cyber attacks without excess overhead. This paper describes POMDP formulation including the use of an absorbing final state and attack penalty scaling factor to abstract defender-defined priorities into the model. An autonomous agent leverages the POMDP to select the optimal defense based on assessed cyber-attack phase. We offer an example formulation wherein attack suppression of greater than 99% and system availability of greater than 94% were maintained even as probability of detection of attack phase dropped to 74%.
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Cybersecurity and Software Assurance
dc.subject cybersecurity
dc.subject markov models
dc.subject moving target
dc.title The use of partially observable Markov decision processes to optimally implement moving target defense
dc.identifier.doi 10.24251/HICSS.2021.840
prism.startingpage 6986
Appears in Collections: Cybersecurity and Software Assurance


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