DEEP LEARNING-BASED CONTINUOUS AUTHENTICATION IN ZEROTRUST ENTERPRISE ENVIRONMENTS

Abstract

The rapid expansion of remote work, cloud services, and distributed enterprise infrastructures has significantly increased cybersecurity risks, rendering traditional perimeter-based security models inadequate. Zero-Trust Architecture (ZTA) has emerged as a modern security paradigm that assumes no implicit trust and continuously verifies user identity and device integrity. However, conventional authentication mechanisms such as passwords and one-time verification methods are insufficient to ensure persistent security throughout a user session. This paper proposes a Deep Learning-Based Continuous Authentication Framework tailored for Zero-Trust enterprise environments. The system leverages behavioral biometrics, including keystroke dynamics, mouse movements, and user interaction patterns, to continuously verify user identity in real time. Advanced deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) are employed to model temporal and spatial behavioral patterns. The framework integrates risk-based scoring and adaptive access control policies to dynamically enforce authentication decisions. Experimental evaluation demonstrates improved detection accuracy, reduced false acceptance rates, and minimal user disruption compared to traditional authentication systems. The proposed solution strengthens enterprise security by enabling adaptive, non-intrusive, and real-time identity verification aligned with Zero-Trust principles