Active Authentication Using Behavioral Biometrics and Machine Learning

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).