Abstract

Traditional authentication mechanisms rely on static credentials and session-based validation, which are insufficient against modern threats such as session hijacking and credential misuse. Continuous authentication addresses this gap by validating user identity throughout an active session; however, practical deployment introduces challenges related to accuracy, usability, and system complexity.

This work presents a deep learning–enabled multimodal continuous authentication system that combines behavioral and contextual signals to continuously assess user identity. The proposed architecture applies deep learning–based feature extraction and fusion to improve authentication confidence while minimizing user friction. The system is designed with enterprise constraints in mind, including latency, privacy, and scalability.

Experimental evaluation indicates that multimodal fusion improves robustness compared to single-modality approaches. Beyond model performance, this work discusses architectural trade-offs and deployment considerations relevant to modern identity and access management platforms.