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Active Authentication on Mobile Devices via Stylometry, Application Usage, Web Browsing, and GPS Location
Journal article   Open access   Peer reviewed

Active Authentication on Mobile Devices via Stylometry, Application Usage, Web Browsing, and GPS Location

Lex Fridman, Steven Weber, Rachel Greenstadt and Moshe Kam
IEEE systems journal, v 11(2), pp 513-521
Jun 2017
DOI: https://doi.org/10.1109/JSYST.2015.2472579
url
https://arxiv.org/abs/1503.08479View
Submitted,  Open

Abstract

Active authentication Androids application usage patterns Authentication behavioral biometrics Biometrics (access control) decision fusion Global Positioning System GPS location Humanoid robots insider threat intrusion detection Keyboards Mobile handsets multimodal biometric systems stylometry web browsing behavior
Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this paper, we collect and analyze behavioral biometrics data from 200 subjects, each using their personal Android mobile device for a period of at least 30 days. This data set is novel in the context of active authentication due to its size, duration, number of modalities, and absence of restrictions on tracked activity. The geographical colocation of the subjects in the study is representative of a large closed-world environment such as an organization where the unauthorized user of a device is likely to be an insider threat: coming from within the organization. We consider four biometric modalities: 1) text entered via soft keyboard, 2) applications used, 3) websites visited, and 4) physical location of the device as determined from GPS (when outdoors) or WiFi (when indoors). We implement and test a classifier for each modality and organize the classifiers as a parallel binary decision fusion architecture. We are able to characterize the performance of the system with respect to intruder detection time and to quantify the contribution of each modality to the overall performance.

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92 citations in Web of Science
123 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Information Systems
Engineering, Electrical & Electronic
Operations Research & Management Science
Telecommunications
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