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Multi-modal decision fusion for continuous authentication
Journal article   Peer reviewed

Multi-modal decision fusion for continuous authentication

Lex Fridman, Ariel Stolerman, Sayandeep Acharya, Patrick Brennan, Patrick Juola, Rachel Greenstadt and Moshe Kam
Computers & electrical engineering, v 41(C)
Jan 2015

Abstract

Active authentication Behavioral biometrics Decision fusion Distributed communication Multimodal biometric systems Security and privacy
[Display omitted] •Behavioral biometrics: keystroke dynamics, mouse movement, stylometry.•A parallel binary decision fusion architecture with 11 sensors.•A dataset collected from 67 users each working in an office environment for a week.•Achieve below 1% error rates (FAR, FRR) after only 30s of activity.•Characterize robustness of system to adversarial attacks. Active authentication is the process of continuously verifying a user based on their on-going interaction with a computer. In this study, we consider a representative collection of behavioral biometrics: two low-level modalities of keystroke dynamics and mouse movement, and a high-level modality of stylometry. We develop a sensor for each modality and organize the sensors as a parallel binary decision fusion architecture. We consider several applications for this authentication system, with a particular focus on secure distributed communication. We test our approach on a dataset collected from 67 users, each working individually in an office environment for a period of approximately one week. We are able to characterize the performance of the system with respect to intruder detection time and robustness to adversarial attacks, and to quantify the contribution of each modality to the overall performance.

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Hardware & Architecture
Computer Science, Interdisciplinary Applications
Engineering, Electrical & Electronic
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