Authors:
Aratrika Ray
1
;
Daqing Hou
1
;
Stephanie Schuckers
1
and
Abbie Barbir
2
Affiliations:
1
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, New York, U.S.A.
;
2
Mobile Security Group, CVS Health, U.S.A.
Keyword(s):
Behavioral Biometrics, Continuous Authentication, Android Smartphones, Acceleration, Angular Velocity, Support Vector Machine, Weighted Score Fusion, Likelihood Ratio-based Score Fusion.
Abstract:
Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as a password, PIN, fingerprint, iris, or face. But these authentication types are prone to a wide attack vector and worse still, once compromised, fail to protect the user’s account and data. In contrast, continuous authentication, based on traits of human behavior, can offer additional security measures in the device to authenticate against unauthorized users, even after the entry-point and one-time authentication has been compromised. To this end, we have collected a new data-set of multiple behavioral biometric modalities (49 users) when a user fills out an account recovery form in sitting using an Android app. These include motion events (acceleration and angular velocity), touch and swipe events, keystrokes, and pattern tracing. In this paper, we focus on authentication based on motion events by evaluating a set of score level fusion techniques to authenticate users based on the accel
eration and angular velocity data. The best EERs of 2.4% and 6.9% for intra- and inter-session respectively, are achieved by fusing acceleration and angular velocity using Nandakumar et al.’s likelihood ratio (LR) based score fusion.
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