Multi-Motion Spatio-temporal Graph-Based User Behavior Representation for Enhanced Smartphone Security

As central hubs of the Internet of Everything, smart phones integrate essential functions such as payments, navigation, and IoT connectivity. However, this expanded functionality also heightens security risks. Motion dynamics biometrics, which utilizes motion patterns from user-phone interactions captured via multi-sensor data, has emerged as a promising solution for smartphone security. Offering continuous and unobtrusive protection by analyzing natural user interactions, it still faces challenges in effectively modeling the complex spatio-temporal dynamics between the user and the phone within multi-sensor data. This paper focuses on leveraging graph neural networks (GNNs) to enhance user behavior modeling for smartphone security protection by capturing the relationships within motion sensor data, but it is non-trivial due to the characteristics of complexity, asynchrony, and temporal dependencies of multi motion sensor data. Towards this end, we propose MotionGNN, a multi-motion spatial-temporal graph based behavior modeling framework for user identification and authentication. Specifically, MotionGNN first divides the input multi-motion sensor data into a sequence of segments adaptively by developing a context-aware data segmentation method. Then, MotionGNN constructs fully connected spatio-temporal graphs to model sensor dependencies and temporal dynamics. Finally, windowing graph convolutions are adopted to learn user behavior representations. To evaluate the performance of MotionGNN, we collect a large-scale dataset from real-world scenarios. Extensive experiments demonstrate the state-of-the-art performance of MotionGNN in user identi fication and authentication tasks. We also test MotionGNN for 7 days on smartphones, showing high authentication accuracy with minimal battery and memory usage, making it a reliable solution for smartphone security protection.