Poster Abstract: Uncovering Mobile User Gait Patterns Through Contactless RF Channels

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.