Using Recurrent Neural Networks for Continuous Authentication through Gait Analysis

Abstract 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 stages of the continuous authentication, starting from data collection, to data preprocessing, classification and policy enforcement. The letter will puts its emphasis on 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, it 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.