Cross-Modal Deep Neural Networks based Smartphone Authentication for Intelligent Things System

Abstract

Nowadays, identity authentication technology, including biometric identification features such as iris and fingerprints, plays an essential role in the safety of intelligent devices. However, it cannot implement real-time and continuous identification of user identity. This paper presents a framework for user authentication from motion signals such as accelerometers and gyroscope signals powered received from smartphones. The proposed innovation scheme including i) a data preprocessing, ii) a novel feature extraction and authentication scheme based on a cross-modal deep neural network by applying a time-distributed Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models. The experimental results of the proposed scheme show the advantage of our approach against methods.