# Intelligent Zero Trust Architecture for 5G/6G Networks: Principles, Challenges, and the Role of Machine Learning in the context of O-RAN

arXiv: https://arxiv.org/abs/2105.01478 · HTML: https://mlsanity.com/paper/2105.01478/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 12 of 12)

- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Graph Neural Networks: Architectures, Stability and Transferability](https://mlsanity.com/paper/2008.01767/) (2020)
- [A Survey of Adversarial Learning on Graphs](https://mlsanity.com/paper/2003.05730/) (2020)
- [Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/1901.07947/) (2019)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [Recurrent Space-time Graph Neural Networks](https://mlsanity.com/paper/1904.05582/) (2019)
- [Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding](https://mlsanity.com/paper/2004.10715/) (2020)
- [Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier](https://mlsanity.com/paper/1904.01352/) (2019)
- [Artificial Intelligence-Defined 5G Radio Access Networks](https://mlsanity.com/paper/1811.08792/) (2018)
- [Augmenting Zero Trust Architecture to Endpoints Using Blockchain: A State-of-The-Art Review](https://mlsanity.com/paper/2104.00460/) (2021)
- [Challenges of Multi-Factor Authentication for Securing Advanced IoT (A-IoT) Applications](https://mlsanity.com/paper/1901.06977/) (2019)

## Built on by (most cited first, 9 of 9)

- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach](https://mlsanity.com/paper/2303.03349/) (2023)
- [MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio](https://mlsanity.com/paper/2204.04507/) (2022)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Evolution of AI Agent Registry Solutions: Centralized, Enterprise, and Distributed Approaches](https://mlsanity.com/paper/2508.03095/) (2025)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks: Research Directions for Security and Optimal Control](https://mlsanity.com/paper/2204.01950/) (2022)
- [Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things](https://mlsanity.com/paper/2505.23792/) (2025)

## Abstract (arXiv)

In this position paper, we discuss the critical need for integrating zero trust (ZT) principles into next-generation communication networks (5G/6G). We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components. While network virtualization, software-defined networking (SDN), and service-based architectures (SBA) are key enablers of 5G networks, operating in an untrusted environment has also become a key feature of the networks. Further, seamless connectivity to a high volume of devices has broadened the attack surface on information infrastructure. Network assurance in a dynamic untrusted environment calls for revolutionary architectures beyond existing static security frameworks. To the best of our knowledge, this is the first position paper that presents the architectural concept design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks. We introduce key ZT principles as real-time Monitoring of the security state of network assets, Evaluating the risk of individual access requests, and Deciding on access authorization using a dynamic trust algorithm, called MED components. To ensure ease of integration, the envisioned architecture adopts an SBA-based design, similar to the 3GPP specification of 5G networks, by leveraging the open radio access network (O-RAN) architecture with appropriate real-time engines and network interfaces for collecting necessary machine learning data. Therefore, this work provides novel research directions to design machine learning based components that contribute towards i-ZTA for the future 5G/6G networks.