Science Daily August 14, 2019
Researchers at the Southern Methodist University investigate the capability of mobile phone sensor arrays, using audio and motion sensor data, for classifying keystrokes that occur on a keyboard in proximity to phones around a table, as would be common in a meeting. They developed a system of mixed convolutional and recurrent neural networks and deployed the system in a human subjects experiment with 20 users typing naturally while talking. Using leave-one-user-out cross validation, they found that mobile phone arrays have the ability to detect 41.8% of keystrokes and 27% of typed words correctly in such a noisy environment. They concluded that in order to launch a successful attack the attacker would need advanced knowledge of the table from which a user types, and the style of keyboard on which a user type. These constraints greatly limit the feasibility of such an attack to highly capable attackers and we therefore conclude threat level of this attack to be low, but non-zero…read more. TECHNICAL ARTICLE