DRL-Based Adaptive Multidomain Feature Fusion for Continuous Authentication on Smartphones
Yantao Li, Yuhang Yang, Shaojiang Deng, Hongyu Huang · IEEE Internet of Things Journal · 2025
In today’s digital era, ensuring the security of mobile devices is of critical importance. Sensor-based continuous authentication has emerged as an effective approach for protecting personal information on mobile devices. However, most existing systems rely primarily on time-domain features, overlooking valuable information from other domains and leading to incomplete feature representation. In this paper, we propose AMDFAuth, a deep reinforcement learning (DRL)-based Adaptive Multi-Domain Feature Fusion For continuous Authentication on smartphones that integrates a multi-domain feature extraction network with an adaptive feature fusion mechanism based on DRL. During user registration, AMDFAuth implicitly collects standardized behavioral data via built-in accelerometers and gyroscopes, and pre-trains a Diffusion Transformer (DiT) model. Through transfer learning, we integrate two additional feature extraction branches with the pre-trained DiT to construct a multi-domain network that captures time-domain, wavelet-domain, and key latent features. These features are then adaptively fused using DRL, enabling joint optimization of the feature extraction modules, fusion network, and an MLP classifier for user identification. During continuous authentication, real-time sensor data are collected and processed by the trained network and classifier to verify user identification. Extensive evaluations on our dataset demonstrate that AMDFAuth achieves 98.52% accuracy and an Equal Error Rate (EER) of 0.94% using a 2-second time window and 10 unseen users. These results highlight the system’s excellent accuracy, robustness, and generalization capability in real-world mobile authentication scenarios.