mlsanityData to 7 Oct 2026

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

arXiv 2105.01478 ↗First posted 4 May 2021cs.NI cs.LGcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 12 of its 46 references that are in the map. First posted on arXiv 4 May 2021.

Builds on

12 of 46 references.

References · 12

  1. 2016RL2: Fast Reinforcement Learning via Slow Reinforcement Learningcited by 490
  2. 2016Learning to reinforcement learncited by 369
  3. 2020Graph Neural Networks: Architectures, Stability and Transferabilitycited by 79
  4. 2020A Survey of Adversarial Learning on Graphscited by 42
  5. 2019Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Surveycited by 27
  6. 20206G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunitiescited by 24
  7. 2019Recurrent Space-time Graph Neural Networkscited by 19
  8. 2020Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfoldingcited by 16
  9. 2019Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifiercited by 14
  10. 2018Artificial Intelligence-Defined 5G Radio Access Networkscited by 2
  11. 2021Augmenting Zero Trust Architecture to Endpoints Using Blockchain: A State-of-The-Art Reviewcited by 2
  12. 2019Challenges of Multi-Factor Authentication for Securing Advanced IoT (A-IoT) Applicationscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 1; 2024: 1; 2025: 2; 2026: 1.

9 papers.