Deep Learning Models for Continuous Authentication on Mobile Devices
- 1st Edition - January 1, 2027
- Latest edition
- Authors: Yantao Li, Qingguo Lü, Huafeng Qin, Hailong Hu
- Language: English
Sensor-based continuous authentication has emerged as a critical approach for strengthening mobile security, enabling persistent user verification without disrupting device usage.… Read more
Description
Description
Sensor-based continuous authentication has emerged as a critical approach for strengthening mobile security, enabling persistent user verification without disrupting device usage. However, the field faces significant hurdles, including limited training data, complex feature representation, environmental noise, and the strict resource constraints of mobile hardware.
Deep Learning Models for Continuous Authentication on Mobile Devices provides a unified and structured treatment of data-driven continuous authentication, presenting a systematic study of sensor-based continuous authentication on mobile devices, focusing on modern machine learning and deep learning techniques. It guides readers in designing, analyzing, and deploying reliable systems that effectively balance security, robustness, and computational efficiency. Featuring data augmentation strategies for data scarcity, multi-sensor feature fusion, discriminative feature learning via two-stream CNNs, data synthesis using conditional Wasserstein GANs, lightweight networks for efficient deployment, neural architecture search for automated optimization, and neuromorphic computing with spiking neural networks,
Deep Learning Models for Continuous Authentication on Mobile Devices balances methodological rigor with practical system design, offering robust solutions for real-world mobile security.
Deep Learning Models for Continuous Authentication on Mobile Devices provides a unified and structured treatment of data-driven continuous authentication, presenting a systematic study of sensor-based continuous authentication on mobile devices, focusing on modern machine learning and deep learning techniques. It guides readers in designing, analyzing, and deploying reliable systems that effectively balance security, robustness, and computational efficiency. Featuring data augmentation strategies for data scarcity, multi-sensor feature fusion, discriminative feature learning via two-stream CNNs, data synthesis using conditional Wasserstein GANs, lightweight networks for efficient deployment, neural architecture search for automated optimization, and neuromorphic computing with spiking neural networks,
Deep Learning Models for Continuous Authentication on Mobile Devices balances methodological rigor with practical system design, offering robust solutions for real-world mobile security.
Key features
Key features
- Introduces representative sensor-based continuous authentication methods on mobile devices, spanning data augmentation, feature fusion, convolutional and generative models, automated architecture search, and neuromorphic learning, offering comprehensive guidance for students and researchers
- Presents practical strategies to address critical challenges in the field, including limited training data, inter-user behavioral variability, robustness to environmental noise and mimic behaviors, and the requirements for efficient deployment on mobile platforms
- Includes systematic experimental analysis and implementation insights derived from both public and real-world datasets, helping practitioners understand the performance of continuous authentication methods in practical scenarios and design their own effective security solutions
Readership
Readership
Graduate students, senior undergraduate students, researchers, and technologists in the fields of mobile security, authentication, smartphone sensors and behavioral biometrics
Table of contents
Table of contents
1. SensorAuth: Data Augmentation for Smartphone Authentication
2. FusionAuth: Feature Fusion Strategies for Mobile Authentication
3. SCANet: Two-Stream CNNs for Multimodal Behavioral Biometrics
4. CAGANet: GAN-Enhanced CNN Models for Robust Authentication
5. DeFFusion: Deep Feature Fusion with Convolutional Networks
6. SearchAuth: Neural Architecture Search for Authentication Model
7. ADFFDA: Adaptive Deep Feature Fusion with Augmented Data
8. SNNAuth: Spiking Neural Networks for Efficient Authentication
2. FusionAuth: Feature Fusion Strategies for Mobile Authentication
3. SCANet: Two-Stream CNNs for Multimodal Behavioral Biometrics
4. CAGANet: GAN-Enhanced CNN Models for Robust Authentication
5. DeFFusion: Deep Feature Fusion with Convolutional Networks
6. SearchAuth: Neural Architecture Search for Authentication Model
7. ADFFDA: Adaptive Deep Feature Fusion with Augmented Data
8. SNNAuth: Spiking Neural Networks for Efficient Authentication
Product details
Product details
- Edition: 1
- Latest edition
- Published: January 1, 2027
- Language: English
About the authors
About the authors
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Yantao Li
Yantao Li received the Ph.D. degree in computer science and technology from Chongqing University, Chongqing, China, in December 2012. He is currently a tenure-track Assistant Professor with the College of Computer Science, Chongqing University, Chongqing, China. He received the Best Paper Award from IEEE Internet Computing in 2022. He was a recipient of the Outstanding Ph.D. Thesis Award, Chongqing, in 2014, and the Outstanding Master's Thesis Award, in 2011. His research interests include mobile computing and security, the Internet of Things, sensor networks, and ubiquitous computing. Prof. Li currently serves as an Associate Editor for the IEEE Internet of Things Journal (IoT-J). His main research interests include machine learning, networked control systems, and decentralized algorithm. He has published more than 40 research papers.
Affiliations and expertise
College of Computer Science, Chongqing University, Chongqing, ChinaQL
Qingguo Lü
Qingguo Lü is a Graduate Research Assistant at Southwest University, Chongqing, China, where he is currently pursuing his Ph.D. degree in Computational Intelligence and Information Processing. His research interests include privacy protection of networked systems, Distributed Optimization, Neurodynamics, and Smart Grids.
Affiliations and expertise
Associate Researcher, College of Computer Science, Chongqing University, Chongqing, ChinaHQ
Huafeng Qin
Huafeng Qin received the B.Sc. degree from the School of Mathematics and Physics and the M.Eng. degree from the College of Electronic and Automation, Chongqing University of Technology, China, and the Ph.D. degree from the College of Opto-Electronic Engineering, Chongqing University, China. He was a visiting student at Nanyang Technological University, Singapore, for 12 months, and subsequently a postdoctoral researcher for two years at Université Paris-Saclay, France. He is currently a Professor with the National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, China. His research interests include biometrics (e.g., vein, face, and gait recognition) and machine learning.
Affiliations and expertise
Chongqing Technology and Business University, Chongqing, ChinaHH
Hailong Hu
Hailong Hu received his Ph.D. degree from the University of Luxembourg, and both his Master and Bachelor degrees from Southwest University. He is currently an Assistant Professor at Chongqing Technology and Business University. His research interests include trustworthy AI and biometrics. His work has been published in leading international journals and conferences such as TIFS, TAI, TOSN, and IoT-J. He has received two Best Paper Honorable Mention Awards (ACSAC 2021 and IEEE NAS 2018). He also serves as a reviewer for journals and conferences such as TIFS, TDSC, TOPS, PR, and CCBR.
Affiliations and expertise
Chongqing Technology and Business University, Chongqing, China