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dc.contributor.advisorRonald L. Rivest.en_US
dc.contributor.authorRossides, Michalisen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-03-06T15:45:52Z
dc.date.available2014-03-06T15:45:52Z
dc.date.copyright2013en_US
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/85491
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 59-60).en_US
dc.description.abstractIn this thesis, we extend the game theoretical analysis of the FlipIt game¹. The game was first articulated and analyzed by Marten van Dijk, Ari Juels, Alina Oprea and Ronald L. Rivest. FlipIt (or otherwise The Game of "Stealthy Takeover") is a game-theoretic framework for modeling computer security scenarios. Such scenarios include targeted attacks, cryptographic key rotation, password changing policies and cloud auditing. What we are particularly interested in are situations in which an attacker repeatedly takes control over a system or critical resource completely, learns all its secret information and this is not immediately detected by the system owner. As mentioned in the original paper', FlipIt is a two-player game between an attacker and a defender, in which both players try to control a shared resource. The players do not know who is currently in charge of the resource until they move; this is why it is called "Stealthy Takeover." Moreover, each player pays a specific cost every time he moves. The objective of each player is to maximize his benefit; this involves controlling the resource for as large fraction of time as possible and, at the same time, minimizing the total move cost. Here, we are only considering scenarios in which the defender plays with a renewal strategy; in particular, the intervals between his consecutive moves are given by a fixed probability distribution. However, the attacker can be more sophisticated than that and deploy adaptive strategies; i.e. he moves based on feedback received during the game. The main strategy we are considering for the defender is the biperiodic as stated in Section 4. For the attacker, we are considering various non-adaptive and adaptive strategies. Finally, we compare it to the relevant strategies already analyzed in the original FlipIt paper¹.en_US
dc.description.statementofresponsibilityby Michalis Rossides.en_US
dc.format.extent76 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleExtending the analysis of the FlipIt gameen_US
dc.title.alternativeGame theory in computer science : extending the FlipIt gameen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc870998854en_US


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