PersonalA: A Lightweight Implicit Authentication System Based on Customized User Behavior Selection

Motivated by the great potential of implicit and seamless user authentication, we attempt to build an implicit authentication (IA) system with adaptive sampling that automatically selects dynamic sets of activities for user behavior extraction. 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. In addition to dynamic behavior extraction, the proposed implicit authentication system differs from the existing systems in terms of energy efficiency for battery-powered mobile devices. Since implicit authentication systems including the proposed one rely on machine learning, the expensive training process needs be outsourced to the remote server. However, mobile devices may not always have reliable network connections to send real-time data to the server for training. We overcome this limitation by proposing a W-layer, an overlay that provides a practical and energy-efficient solution for implicit authentication on mobile devices. We implemented partially labeled Dirichlet allocation (PLDA) on the server side for more accurate feature extraction, and achieved 93.3 percent precision and 98.6 percent accuracy in the synthetic dataset. Furthermore, we tested the power consumption of the smartphones used for our experiments and found that our method consumed 14.5 percent of the devices' total battery usage.