Zero-Trust Reinforcement Learning for Autonomous Network Access Decisions

Abstract

The increasing complexity of modern network environments has made traditional security models, such as perimeter-based defenses, insufficient for mitigating advanced cyber threats. The Zero-Trust security model, which assumes no trust for any user or device, regardless of location, has gained significant traction in securing network infrastructures. This paper proposes a novel Zero-Trust Reinforcement Learning (RL) framework for autonomous network access decisions, aiming to enhance security and optimize resource management. By integrating RL with a Zero-Trust architecture, the system dynamically learns optimal access control policies based on real-time network behavior, user profiles, and device context. The proposed framework employs a multi-agent reinforcement learning (MARL) approach to continuously evaluate and adjust access decisions, minimizing the risk of unauthorized access while maintaining operational efficiency. Through simulation-based experiments, we demonstrate that the RL-driven Zero-Trust model outperforms traditional rule-based systems by adapting to evolving network conditions and threat landscapes. This approach not only strengthens security but also introduces flexibility and scalability, enabling the system to autonomously respond to emerging threats without human intervention. The results highlight the potential of Zero-Trust Reinforcement Learning as a transformative solution for next-generation network security in dynamic and complex environments.