This letter presents a novel framework for continuous user authentication of mobile devices based on gait analysis, exploiting inertial sensors and Recurrent Neural Network for deep-learning based classification. The proposed framework handles all the continuous authentication stages, starting from data collection to data preprocessing, classification, and policy enforcement. The letter will emphasize the data analysis aspects, discussing the methodologies used to improve the quality of classification, including data augmentation and a sliding window interval approach for improved training. Furthermore, will be discussed the enforcement, which is based on the Usage Control paradigm for continuous policy enforcement. A set of real experiments will demonstrate the effectiveness and efficiency of the proposed framework.

这封信提出了一种基于步态分析的移动设备连续用户身份验证的新框架，该框架利用惯性传感器和递归神经网络进行基于深度学习的分类。所提出的框架处理了从数据收集到数据预处理，分类和策略执行的所有连续身份验证阶段。这封信将重点介绍数据分析方面，讨论用于提高分类质量的方法，包括数据扩充和滑动窗间隔方法以改进训练。此外，将讨论执行，该执行基于用于连续策略执行的“使用控制”范式。一组实际实验将证明所提出框架的有效性和效率。