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.

• 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