Dynamic Behavioral Profiling for User Authentication Using Binary Pressure Sequences and Grip Patterns via a Soft Stacking Ensemble

Mobile authentication is essential for protecting personal data and payment authorization, yet prevailing approaches face two fundamental limitations: the nonrevocability of physiological biometrics once compromised and the susceptibility of knowledge‐based authentication to observation‐ and trace‐based attacks. This study proposes a next‐generation mobile authentication framework based on dynamic behavioral profiling, in which binary pressure sequences and grip‐position patterns are jointly modeled. The hardware design measures fine spatiotemporal pressure distributions using symmetrically mounted fabric‐based strip sensor arrays along the display bracket, and enhances localized pressure‐transfer efficiency via an electrode‐aligned protrusion film. Closely coupled to this sensing design, the algorithm employs a probability‐based soft‐stacking scheme that combines class‐wise predictive probabilities from base learners to mitigate information loss induced by threshold‐based discretization, increasing mean accuracy from 0.802 to 0.857 compared with fixed‐threshold encoding. The proposed framework achieves an AUC of 0.9867 (EER = 0.049) for 16‐class pressure‐sequence authentication and an AUC of 0.9870 (EER = 0.0595) for 6‐class grip‐scenario classification. These results support the feasibility of using touch‐pressure and grip interactions as re‐enrollable behavioral authentication signals and provide preliminary participant‐level validation of the framework. Further investigation involving larger and more diverse populations and real‐world settings is needed to assess its potential for continuous authentication.