LATIDIA · Ciberseguridad

Modelado de deriva conductual basado en el aprendizaje profundo para la autenticación biométrica continua

arXiv: 2610.40398v1Tipo de anuncio: nuevo Resumen: Esta investigación propone un sistema de autenticación dinámica de pulsaciones de teclas de dos niveles que proporciona seguridad de defensa en profundidad a través del monitoreo continuo de la deriva del comportamiento. Nivel 1

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arXiv:2610.40398v1 Announce Type: new Abstract: This research proposes a two-level keystroke dynamics authentication system that provides defense-in-depth security through continuous behavioral drift monitoring. Level 1 employs fixed-text password authentication using Temporal Convolutional Networks (TCN) with contrastive learning and population-based training (PBT), achieving 3.77% EER for high-security login verification. Upon successful authentication, Level 2 continuously monitors user typing during natural free-text sessions using a 4-Model Deep Learning Ensemble. If suspicious behavioral drift is detected during monitoring, the system re-authenticates the user by returning to Level 1, providing a feedback loop against session hijacking and behavioral anomalies. The system explicitly models long-term behavioral drift through the fixed-text component's PBT mechanism, which adapts