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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Preface - p. xiii 7. Acknowledgements - p. xv 8. 1: SensorAuth: data augmentation for smartphone authentication - p. 1 9. 2: FusionAuth: feature fusion strategies for mobile authentication - p. 23 10. 3: SCANet: two-stream CNNs for multimodal behavioral biometrics - p. 33 11. 4: CAGANet: GAN-enhanced CNN models for robust authentication - p. 61 12. 5: DeFFusion: deep feature fusion with convolutional networks - p. 85 13. 6: SearchAuth: neural architecture search for authentication model - p. 107 14. 7: ADFFDA: adaptive deep feature fusion with augmented data - p. 131 15. 8: SNNAuth: spiking neural networks for efficient authentication - p. 161 16. Bibliography - p. 181 17. Index - p. 193
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Preface - p. xiii 7. Acknowledgements - p. xv 8. 1: SensorAuth: data augmentation for smartphone authentication - p. 1 9. 2: FusionAuth: feature fusion strategies for mobile authentication - p. 23 10. 3: SCANet: two-stream CNNs for multimodal behavioral biometrics - p. 33 11. 4: CAGANet: GAN-enhanced CNN models for robust authentication - p. 61 12. 5: DeFFusion: deep feature fusion with convolutional networks - p. 85 13. 6: SearchAuth: neural architecture search for authentication model - p. 107 14. 7: ADFFDA: adaptive deep feature fusion with augmented data - p. 131 15. 8: SNNAuth: spiking neural networks for efficient authentication - p. 161 16. Bibliography - p. 181 17. Index - p. 193
