Countering Malicious Documents and Adversarial Learning

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dc.contributor.advisorStavrou, Angelos
dc.contributor.authorSmutz, Charles
dc.creatorSmutz, Charles
dc.date.accessioned2017-01-29T01:17:28Z
dc.date.available2017-01-29T01:17:28Z
dc.date.issued2016
dc.description.abstractIn order to exploit the large number of vulnerabilities offered by user
dc.format.extent169 pages
dc.identifier.urihttps://hdl.handle.net/1920/10618
dc.language.isoen
dc.rightsCopyright 2016 Charles Smutz
dc.subjectInformation technology
dc.subjectAdversarial learning
dc.subjectContent randomization
dc.subjectMalware
dc.subjectMutual agreement
dc.subjectRandom Forests
dc.titleCountering Malicious Documents and Adversarial Learning
dc.typeDissertation
thesis.degree.disciplineInformation Technology
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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