Cross-Modal Deep Neural Networks based Smartphone Authentication for Intelligent Things System
Tran Anh Khoa, Dinh Nguyen The Truong, Duc Ngoc Minh Dang · 2021
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.