A dynamic context-aware continuous authentication method for mobile devices

By employing a dynamic context-aware and multimodal fusion-based identity authentication method, the static efficiency and session drift issues in mobile device identity authentication are resolved. This achieves high-precision, low-power identity recognition, adapts to dynamic changes in user behavior patterns, and improves device battery life and user experience.

CN121786811BActive Publication Date: 2026-07-10WENZHOU UNIV OUJIANG COLLEGE
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
WENZHOU UNIV OUJIANG COLLEGE
Filing Date
2026-03-04
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing mobile device authentication methods are inadequate in terms of static efficiency and session drift, resulting in energy waste and high false positive rates, and are unable to adapt to dynamic changes in user behavior patterns.

Method used

A dynamic context-aware continuous identity authentication method is adopted. An identity authentication model is constructed through automatic context labeling, multimodal fusion and dynamic neural pruning. The sliding window is used to process time-series data and the computational complexity is adjusted in real time to adapt to different contexts. The model includes a feature mapping layer, a two-stream context-gated temporal patch encoder and a gated cross-modal attention fusion layer, and identity authentication is performed by combining motion intensity and interaction density.

Benefits of technology

It significantly reduces the false rejection rate during session drift, achieves high-precision and low-power identity recognition, adapts to dynamic changes in real-world scenarios, and improves the battery life and user experience of mobile devices.

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Abstract

This invention pertains to the field of mobile device authentication and discloses a dynamic context-aware continuous authentication method for mobile devices. It constructs a two-dimensional behavior quantizer based on motion intensity and interaction density to define the physical boundaries of pure motion, pure interaction, and hybrid scenarios in real time. A gating unit controlled by MI / ID signals is introduced to automatically generate masks in extreme scenarios such as pure motion or pure interaction to suppress ineffective attention calculations for missing modalities, enabling full feature interaction only in hybrid scenarios. A deterministic mapping rule from physical context state to network sparsity is established. Based on the real-time calculated MI / ID physical metrics, the system directly indexes the corresponding sparse sub-paths in the pre-trained multi-pruning rate network, achieving millisecond-level zero-overhead state switching while ensuring security.
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