  Prevailing user authentication schemes on smartphones rely on explicit user
interaction, where a user types in a passcode or presents a biometric cue such
as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to
the users, such authentication mechanisms pose security and privacy concerns.
Passive authentication systems can tackle these challenges by frequently and
unobtrusively monitoring the user's interaction with the device. In this paper,
we propose a Siamese Long Short-Term Memory network architecture for passive
authentication, where users can be verified without requiring any explicit
authentication step. We acquired a dataset comprising of measurements from 30
smartphone sensor modalities for 37 users. We evaluate our approach on 8
dominant modalities, namely, keystroke dynamics, GPS location, accelerometer,
gyroscope, magnetometer, linear accelerometer, gravity, and rotation sensors.
Experimental results find that, within 3 seconds, a genuine user can be
correctly verified 97.15% of the time at a false accept rate of 0.1%.
