TY - GEN
T1 - Poster Abstract
T2 - 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024
AU - Yang, Huanqi
AU - Li, Xinyue
AU - Chen, Jiahuan
AU - Han, Mingda
AU - Xu, Weitao
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2024
Y1 - 2024
N2 - Gait-based authentication has risen to prominence for its distinctive advantages, becoming an essential security mechanism for mobile devices. These devices typically employ Inertial Measurement Units (IMUs) to capture intricate gait patterns for confirming the identity of users. However, our research highlights a vulnerability: the user's gait data on mobile devices is susceptible to interception through a radio frequency (RF) side-channel, potentially allowing unauthorized access. We introduce Gait-Snoop as aproof-of-concept for this novel side-channel attack. Gait-Snoop utilizes the RF signals reflected during a user's walk to extract gait information. It then correlates these RF signal patterns with IMU-derived gait data and employs a robotic arm to replicate the gait, aiming to deceive and unlock the targeted mobile devices. Our comprehensive evaluation of Gait-Snoop on smartphones demonstrates its capability to mimic IMU gait signals, underscoring the effectiveness and potential risks of such side-channel attacks. © 2024 IEEE.
AB - Gait-based authentication has risen to prominence for its distinctive advantages, becoming an essential security mechanism for mobile devices. These devices typically employ Inertial Measurement Units (IMUs) to capture intricate gait patterns for confirming the identity of users. However, our research highlights a vulnerability: the user's gait data on mobile devices is susceptible to interception through a radio frequency (RF) side-channel, potentially allowing unauthorized access. We introduce Gait-Snoop as aproof-of-concept for this novel side-channel attack. Gait-Snoop utilizes the RF signals reflected during a user's walk to extract gait information. It then correlates these RF signal patterns with IMU-derived gait data and employs a robotic arm to replicate the gait, aiming to deceive and unlock the targeted mobile devices. Our comprehensive evaluation of Gait-Snoop on smartphones demonstrates its capability to mimic IMU gait signals, underscoring the effectiveness and potential risks of such side-channel attacks. © 2024 IEEE.
UR - https://www.scopus.com/pages/publications/85198529090
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85198529090&origin=recordpage
U2 - 10.1109/IPSN61024.2024.00049
DO - 10.1109/IPSN61024.2024.00049
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings - ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN
SP - 297
EP - 298
BT - Proceedings - 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2024)
PB - IEEE
Y2 - 13 May 2024 through 16 May 2024
ER -