Energy-efficient W-layer for behavior-based implicit authentication on mobile devices

Abstract

Motivated by the great potential of implicit and seamless user authentication, we attempt to build an efficient middle layer running on mobile devices to support implicit authentication (IA) systems with adaptive sampling. Various activities, such as user location, application usage, user motion, and battery usage have been popular choices to generate behaviors, the soft biometrics, for implicit authentication. Unlike password-based or hard biometric-based authentication, implicit authentication does not require explicit user action or expensive hardware. However, user behaviors can change unpredictably which renders it more challenging to develop systems that depend on them. Various machine learning algorithms have been used to address this challenge. The expensive training process is usually outsourced to the remote server but this can potentially increase the chance of data leakage. In addition, mobile devices may not always have reliable network connections to send real-time data to the server for training. Motivated by these limitations, we propose a W-layer, an overlay that provides an energy-efficient solution for real-time implicit authentication on mobile devices. The size of the data the system needs to collect at different times depends on the legitimacy of the user. This in turn affects how the sampling rate is adjusted which can reduce energy consumption. To evaluate our method, we conducted several experiments on both synthetic and real datasets. The average accuracy of identifying legitimate users is 96.73% using the synthetic dataset and 96.70% using the real dataset. Furthermore, we tested the power consumption on a low-end Nexus S smartphone to obtain a more pessimistic result. We found that our method consumed 14.5% of the device's total battery usage. The power consumption performance is expected to improve significantly on high-end mobile devices.