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Active Authentication Using Behavioral Biometrics and Machine Learning

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dc.contributor.advisor Wechsler, Harry
dc.contributor.author EL MASRI, Ala'a
dc.creator EL MASRI, Ala'a
dc.date.accessioned 2016-09-28T10:23:51Z
dc.date.available 2016-09-28T10:23:51Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/1920/10473
dc.description.abstract Active, or continuous, authentication is gradually gaining grounds as the preferred method of personal authentication. This is due to the limited nature of standard authentication methods that are unable to guarantee user identity beyond initial authentication. While research in the area of active authentication has explored and proposed various techniques to overcome this problem, we present two new behavioral-based biometric models for active authentication that expand on current research in terms of performance and scope using adaptive user profiles and their dynamics over time. The novel active authentication models are complementary to each other and include: (1) Application Commands Streams Authentication Model (ACSAM) and (2) Scrolling Behavior Authentication Model (SBAM).
dc.format.extent 125 pages
dc.language.iso en
dc.rights Copyright 2016 Ala'a EL MASRI
dc.subject Information technology
dc.subject Computer science
dc.subject Active Authentication
dc.subject Behavioral Biometrics
dc.subject Machine Learning
dc.title Active Authentication Using Behavioral Biometrics and Machine Learning
dc.type Dissertation
thesis.degree.level Doctoral
thesis.degree.discipline Information Technology
thesis.degree.grantor George Mason University


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