Active Authentication Using Behavioral Biometrics and Machine Learning

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dc.contributor.advisorWechsler, Harry
dc.contributor.authorEL MASRI, Ala'a
dc.creatorEL MASRI, Ala'a
dc.date.accessioned2016-09-28T10:23:51Z
dc.date.available2016-09-28T10:23:51Z
dc.date.issued2016
dc.description.abstractActive, 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.extent125 pages
dc.identifier.urihttps://hdl.handle.net/1920/10473
dc.language.isoen
dc.rightsCopyright 2016 Ala'a EL MASRI
dc.subjectInformation technology
dc.subjectComputer science
dc.subjectActive authentication
dc.subjectBehavioral biometrics
dc.subjectMachine learning
dc.titleActive Authentication Using Behavioral Biometrics and Machine Learning
dc.typeDissertation
thesis.degree.disciplineInformation Technology
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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