Towards Passive Authentication using Inertia Variations: An Experimental Study on Smartphones

Passive biometrics and behavioral analytics seek to identify users based on their unique patterns of activities. In this paper, we test the feasibility of using time-varying inertia data as passive biometrics to be used for user identification and authentication. We present a deep learning model for inertia pattern recognition that achieved a high accuracy of 87.17%. A fully-connected sequential deep neural network was trained on 6730 sensor data samples, each having 15 features: triaxial measurements from accelerometer, gyroscope, magnetometer, and rotational vector. We further discuss the potential impact of inertia pattern recognition for user identification and authentication.