TY - GEN
T1 - On motion sensors as source for user input inference in smartphones
AU - Shen, Chao
AU - Pei, Shichao
AU - Yu, Tianwen
AU - Guan, Xiaohong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/16
Y1 - 2015/6/16
N2 - A wealth of sensors on smartphone has greatly enriched people's life, but these sensors also brought potential security problems since they allow third-party applications to monitor the motion changes of smartphones. This paper presents an empirical study of analyzing the characteristics of accelerometer and magnetometer data collected from third-party applications to infer user inputs on smartphone. Specifically, an installed application was run as a background process to monitor the data of motion sensors. Accelerometer data was analyzed to detect the occurrence of touch tap actions. Then the accelerometer data and magnetometer data were combined together to build a model for inferring the tap position on touchscreen. Along with common layouts of keyboard or number pad, one can easily obtain users' inputs. Results indicated that users' inputs could be accurately inferred from the data of motion sensors, with the accuracies of 100% and 80% for tap-action detection and input inference in some cases. We conclude that readings from motion sensor are a powerful side channel for inferring user inputs, and could provide extra avenues for attackers.
AB - A wealth of sensors on smartphone has greatly enriched people's life, but these sensors also brought potential security problems since they allow third-party applications to monitor the motion changes of smartphones. This paper presents an empirical study of analyzing the characteristics of accelerometer and magnetometer data collected from third-party applications to infer user inputs on smartphone. Specifically, an installed application was run as a background process to monitor the data of motion sensors. Accelerometer data was analyzed to detect the occurrence of touch tap actions. Then the accelerometer data and magnetometer data were combined together to build a model for inferring the tap position on touchscreen. Along with common layouts of keyboard or number pad, one can easily obtain users' inputs. Results indicated that users' inputs could be accurately inferred from the data of motion sensors, with the accuracies of 100% and 80% for tap-action detection and input inference in some cases. We conclude that readings from motion sensor are a powerful side channel for inferring user inputs, and could provide extra avenues for attackers.
UR - https://www.scopus.com/pages/publications/84942543979
U2 - 10.1109/ISBA.2015.7126368
DO - 10.1109/ISBA.2015.7126368
M3 - 会议稿件
AN - SCOPUS:84942543979
T3 - 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015
BT - 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015
Y2 - 23 March 2015 through 25 March 2015
ER -