Countering Malicious Documents and Adversarial Learning
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.description.abstract | In order to exploit the large number of vulnerabilities offered by user | |
dc.format.extent | 169 pages | |
dc.identifier.uri | https://hdl.handle.net/1920/10618 | |
dc.language.iso | en | |
dc.rights | Copyright 2016 Charles Smutz | |
dc.subject | Information technology | |
dc.subject | Adversarial learning | |
dc.subject | Content randomization | |
dc.subject | Malware | |
dc.subject | Mutual agreement | |
dc.subject | Random Forests | |
dc.title | Countering Malicious Documents and Adversarial Learning | |
dc.type | Dissertation | |
thesis.degree.discipline | Information Technology | |
thesis.degree.grantor | George Mason University | |
thesis.degree.level | Ph.D. |
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