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Countering Malicious Documents and Adversarial Learning

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dc.contributor.advisor Stavrou, Angelos
dc.contributor.author Smutz, Charles
dc.creator Smutz, Charles
dc.date.accessioned 2017-01-29T01:17:28Z
dc.date.available 2017-01-29T01:17:28Z
dc.date.issued 2016
dc.identifier.uri https://hdl.handle.net/1920/10618
dc.description.abstract In order to exploit the large number of vulnerabilities offered by user
dc.format.extent 169 pages
dc.language.iso en
dc.rights Copyright 2016 Charles Smutz
dc.subject Information technology en_US
dc.subject adversarial learning en_US
dc.subject content randomization en_US
dc.subject malware en_US
dc.subject mutual agreement en_US
dc.subject Random Forests en_US
dc.title Countering Malicious Documents and Adversarial Learning
dc.type Dissertation
thesis.degree.level Ph.D.
thesis.degree.discipline Information Technology
thesis.degree.grantor George Mason University


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