Secondary User Authentication Based on Mobile Devices Location

2010 ◽  
Author(s):  
Min-Hsao Chen ◽  
Chung-Han Chen
Keyword(s):  
Mobile Devices ◽  
User Authentication ◽  
Secondary User

Breaking Three Remote user Authentication Systems for Mobile Devices

2017 ◽  
Vol 90 (8-9) ◽  
pp. 1179-1190 ◽  
Author(s):  
Wenting Li ◽  
Yaosheng Shen ◽  
Ping Wang
Keyword(s):  
Mobile Devices ◽  
User Authentication ◽  
Remote User Authentication ◽  
Remote User

User authentication on mobile devices: Approaches, threats and trends

2020 ◽  
Vol 170 ◽  
pp. 107118 ◽  
Author(s):  
Chen Wang ◽  
Yan Wang ◽  
Yingying Chen ◽  
Hongbo Liu ◽  
Jian Liu
Keyword(s):  
Mobile Devices ◽  
User Authentication

scholarly journals A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3876 ◽  
Author(s):  
Tiantian Zhu ◽  
Zhengqiu Weng ◽  
Guolang Chen ◽  
Lei Fu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Mobile Devices ◽  
Real World ◽  
Large Scale ◽  
User Authentication ◽  
Mobile User ◽  
Learning System ◽  
Support Vector ◽  
Motion Sensors

With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

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