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Mobile Information Systems
Volume 2017, Article ID 1367064, 14 pages
Research Article

Leveraging Battery Usage from Mobile Devices for Active Authentication

imec-DistriNet, Department of Computer Science, KU Leuven, Leuven, Belgium

Correspondence should be addressed to Davy Preuveneers;

Received 7 September 2016; Revised 16 January 2017; Accepted 14 February 2017; Published 12 March 2017

Academic Editor: Daniele Riboni

Copyright © 2017 Jan Spooren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Active authentication is the practice of continuously verifying the identity of users, based on their context, interactions with a system, and information provided by that system. In this paper, we investigate if battery charge readings from mobile devices can be used as an extra factor to improve active authentication. We make use of a large data set of battery charge readings from real users and construct two computationally inexpensive machine learning classifiers to predict if a user session is authentic: the first one only based on the battery charge at a certain time of day; the second one predicts the authenticity of the user session when a previous, recent battery charge reading is available. Our research shows that a simple two-figure battery charge value can make a useful albeit minor contribution to active authentication.