Abstract
Security for mobile devices have largely focused on the development of trusted hardware and securing software, however these secure platforms are still vulnerable to physical side channel attacks. Side channel attacks bypass secure hardware access controls, exploiting the physical characteristics of devices and onboard sensors to compromise and leak sensitive information. In this paper, we investigate the use of onboard sensors to recover user input on touchscreen interfaces. We evaluate the use of motion and acoustic sensors to categories user interactions with the device and apply machine learning techniques to find a strong correlation between acoustic emanations and user input. The acoustic output of a touch-screen mobile device is used to build a model that predicts user input with up to 86% accuracy in a realistic scenario.