Cross-Modal Temporal Attention Network for Continuous Authentication in Mobile Behavioral Biometrics Mobil Davranişsal Biyometride Sürekli Kimlik Doǧrulama için Çapraz Modal Zamansal Dikkat Aǧi

In this study, a novel deep learning architecture called Cross-Modal Temporal Attention Network (CMTA-Net) is proposed for the problem of continuous authentication based on mobile behavioral biometrics. The model represents user behavior by processing touch, accelerometer, and gyroscope data through separate branches using cross-modal attention and temporal attention mechanisms. In experiments conducted on the HMOG dataset under the leakage-free Leave-One-Session-Out (LOSO) protocol, an EER of 8.17% was achieved at the instant decision level, which was reduced to 5.97% with 20-second decision fusion. The results demonstrate that the proposed approach provides effective and competitive performance for continuous authentication using multimodal mobile sensor data.