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.
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