Mason Archival Repository Service

Active Authentication Using Behavioral Biometrics and Machine Learning

Show simple item record

dc.contributor.advisor Wechsler, Harry EL MASRI, Ala'a
dc.creator EL MASRI, Ala'a 2016-09-28T10:23:51Z 2016-09-28T10:23:51Z 2016
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 en_US
dc.subject Computer science en_US
dc.subject Active Authentication en_US
dc.subject Behavioral Biometrics en_US
dc.subject Machine Learning en_US
dc.title Active Authentication Using Behavioral Biometrics and Machine Learning
dc.type Dissertation Doctoral Information Technology George Mason University

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search MARS


My Account