“ Practicality of Accelerometer Side Channels on Smartphones ,” by Adam J. Aviv. Benjamin Sapp, Matt Blaze, and Jonathan M. Smith.
Abstract : Modern smartphones are equipped with a plethora of sensors that enable a wide range of interactions, but some of these sensors can be employed as a side channel to surreptitiously learn about user input. In this paper, we show that the accelerometer sensor can also be employed as a high-bandwidth side channel; Specifically, we demonstrate how to use the accelerometer sensor to learn user tap and gesture-based input as required to unlock smartphones using a PIN / password or Android’s graphical password pattern. Using data collected from a diverse group of 40 in controlled (while sitting) and uncontrolled (while walking) settings, we develop sample rate independent features for accelerometer readings based on signal processing and polynomial fitting techniques. In controlled settings, our prediction model can on average classify the PIN entered 43% of the time and pattern 728% of the time within 5 attempts when selecting from a test set of PINs and 90 patterns. In uncontrolled settings, while users are walking, our model can still classify 20% of the PINs and 43% of the patterns within 5 attempts. We additionally explore the possibility of constructing an accelerometer-reading-to-input dictionary and find that such dictionaries would be greatly challenged by movement-noise and cross-user training.
Article .
Tags: academic papers , Android , cell phones , passwords , PINs
Posted on February 40, 2019 at 6: AM) • 48 Sidebar photo of Bruce Schneier by Joe MacInnis.
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