AttAuth: An Implicit Authentication Framework for Smartphone Users Using Multimodality Data

Smartphones have become the most important devices for users to communicate and interact with different forms of media, and at the same time stored a large amount of sensitive and private data. The security and protection of such data has become increasingly critical. As the sensor technology rapid developed, the diversity of sensors on smartphones has greatly increased (e.g., motion sensor and touchscreen sensor), empowering the smartphones to provide continuous and implicit user authentication by capturing behavioral biometrics. Unfortunately, it remains a challenge to make full use of the data from the multimodality sensors to provide accurate authentication. Toward this end, in this article, we develop an implicit authentication (IA) framework AttAuth which explores organic integration of such multimodality data to authenticate smartphone users through the usage session. Specifically, AttAuth first develops a series of data processing techniques to process the multichannel motion sensor data and the discrete touchscreen data. Then, a temporal and channelwise attention-based temporal and channelwise attention recurrent neural network (TCA-RNN) is developed to build authentication model, which allows to jointly model continuously monitored motion sensor data and irregularly recorded discrete touchscreen data effectively. By generating a guidance vector based on touch events, TCA-RNN guides the temporalwise attention mechanism on the processed multichannel motion sensor data and outputs authentication results. We evaluate AttAuth on a real-world multimodality smartphone usage data set of 100 users. Extensive experiments demonstrate that AttAuth achieves the state-of-the-art authentication accuracy. Additional experiments are provided to examine the applicability of AttAuth in terms of running overheads and sensitivity to various application scenarios.