Secure and Trustworthy Open Radio Access Network (O-RAN) Optimization: A Zero-Trust and Federated Learning Framework for 6G Networks

Mohammed El-Hajj

doi:10.3390/fi17060233

journal article Open Access May 25, 2025

Secure and Trustworthy Open Radio Access Network (O-RAN) Optimization: A Zero-Trust and Federated Learning Framework for 6G Networks

Mohammed El-Hajj

Future Internet Vol. 17 No. 6 pp. 233 · MDPI AG

View at Publisher Save 10.3390/fi17060233

Abstract

The Open Radio Access Network (O-RAN) paradigm promises unprecedented flexibility and cost efficiency for 6G networks but introduces critical security risks due to its disaggregated, AI-driven architecture. This paper proposes a secure optimization framework integrating zero-trust principles and privacy-preserving Federated Learning (FL) to address vulnerabilities in O-RAN’s RAN Intelligent Controllers (RICs) and xApps/rApps. We first establish a novel threat model targeting O-RAN’s optimization processes, highlighting risks such as adversarial Machine Learning (ML) attacks on resource allocation models and compromised third-party applications. To mitigate these, we design a Zero-Trust Architecture (ZTA) enforcing continuous authentication and micro-segmentation for RIC components, coupled with an FL framework that enables collaborative ML training across operators without exposing raw network data. A differential privacy mechanism is applied to global model updates to prevent inference attacks. We validate our framework using the DAWN Dataset (5G/6G traffic traces with slicing configurations) and the OpenRAN Gym Dataset (O-RAN-compliant resource utilization metrics) to simulate energy efficiency optimization under adversarial conditions. A dynamic DU sleep scheduling case study demonstrates 32% energy savings with <5% latency degradation, even when data poisoning attacks compromise 15% of the FL participants. Comparative analysis shows that our ZTA reduces unauthorized RIC access attempts by 89% compared to conventional O-RAN security baselines. This work bridges the gap between performance optimization and trustworthiness in next-generation O-RAN, offering actionable insights for 6G standardization.

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References

66

[1]

6G Mobile Communication Technology: Requirements, Targets, Applications, Challenges, Advantages, and Opportunities

Mohammed Banafaa, Ibraheem Shayea, Jafri Din et al.

Alexandria Engineering Journal 2023 10.1016/j.aej.2022.08.017

[2]

Lu "6G: A survey on technologies, scenarios, challenges, and the related issues" J. Ind. Inf. Integr. (2020)

[3]

Singh, S.K., Singh, R., and Kumbhani, B. (2020, January 25–28). The evolution of radio access network towards open-RAN: Challenges and opportunities. Proceedings of the 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Seoul, Republic of Korea. 10.1109/wcncw48565.2020.9124820

[4]

Tripathi, N.D., and Shah, V.K. (2025). Fundamentals of O-RAN, John Wiley & Sons. 10.1002/9781394206834

[5]

Polese "Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges" IEEE Commun. Surv. Tutor. (2023) 10.1109/comst.2023.3239220

[6]

Thieu, H.T., Pham, V.Q., Kak, A., and Choi, N. (2023, January 20). Demystifying the Near-real Time RIC: Architecture, Operations, and Benchmarking Insights. Proceedings of the IEEE INFOCOM 2023—IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Hoboken, NJ, USA. 10.1109/infocomwkshps57453.2023.10225852

[7]

Montebugnoli, S., Sabbioni, A., and Foschini, L. (2024, January 8–12). Evaluating Mesh Communications in Disaggregated Near-RT RIC for 5G Open RAN: A Functional and Performance Analysis. Proceedings of the GLOBECOM 2024—2024 IEEE Global Communications Conference, Cape Town, South Africa. 10.1109/globecom52923.2024.10901151

[8]

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

Kang Wei, Jun Li, Ming Ding et al.

IEEE Transactions on Information Forensics and Sec... 2020 10.1109/tifs.2020.2988575

[9]

Liyanage "Open RAN security: Challenges and opportunities" J. Netw. Comput. Appl. (2023) 10.1016/j.jnca.2023.103621

[10]

Nokia Bell Labs (2025, January 12). Open RAN Security: Challenges and Opportunities. Available online: https://www.nokia.com/bell-labs/publications-and-media/publications/open-ran-security-challenges-and-opportunities/.

[11]

Hoang "Security risks and countermeasures of adversarial attacks on AI-driven applications in 6G networks: A survey" J. Netw. Comput. Appl. (2024) 10.1016/j.jnca.2024.104031

[12]

Barreno "The security of Machine Learning" Mach. Learn. (2010) 10.1007/s10994-010-5188-5

[13]

Shokri, R., Stronati, M., Song, C., and Shmatikov, V. (2017, January 22–24). Membership inference attacks against Machine Learning models. Proceedings of the 2017 IEEE Symposium on Security and Privacy, San Jose, CA, USA. 10.1109/sp.2017.41

[14]

European Union (2025, January 15). General Data Protection Regulation (GDPR). Available online: https://gdpr-info.eu/.

[15]

Ramezanpour "Intelligent Zero Trust Architecture for 5G/6G networks: Principles, challenges, and the role of Machine Learning in the context of O-RAN" Comput. Netw. (2022) 10.1016/j.comnet.2022.109358

[16]

Atalay "An OpenRAN security framework for scalable authentication, authorization, and discovery of xapps with isolated critical services" IEEE Trans. Dependable Secur. Comput. (2025) 10.1109/tdsc.2024.3522218

[17]

Djuitcheu, H., Kakani, K., Schotten, H.D., Brunke, D., and Fraunholz, D. (2024). Exploring the implications and methodologies of securing the E2 interface. Authorea Prepr. 10.36227/techrxiv.170862427.72731328/v1

[18]

Differential Privacy for Deep and Federated Learning: A Survey

Ahmed El Ouadrhiri, Ahmed Abdelhadi

IEEE Access 2022 10.1109/access.2022.3151670

[19]

Bonati, L., Polese, M., D’Oro, S., Basagni, S., and Melodia, T. (2022, January 10–13). OpenRAN gym: An open toolbox for data collection and experimentation with AI in O-RAN. Proceedings of the 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA. 10.1109/wcnc51071.2022.9771908

[20]

"O-RAN: Disrupting the virtualized RAN ecosystem" IEEE Commun. Stand. Mag. (2021) 10.1109/mcomstd.101.2000014

[21]

Soltani "Intelligent control in 6G open RAN: Security risk or opportunity?" IEEE Open J. Commun. Soc. (2025) 10.1109/ojcoms.2025.3526215

[22]

Adamczyk "Conflict mitigation framework and conflict detection in O-RAN near-RT RIC" IEEE Commun. Mag. (2023) 10.1109/mcom.018.2200752

[23]

Ngo "RAN Intelligent Controller (RIC): From open-source implementation to real-world validation" ICT Express (2024) 10.1016/j.icte.2024.02.001

[24]

Habibi "Unlocking O-RAN potential: How management data analytics enhances SMO capabilities?" IEEE Open J. Commun. Soc. (2024) 10.1109/ojcoms.2024.3431286

[25]

Habibi, M.A., Han, B., Saimler, M., Pavon, I.L., and Schotten, H.D. (2024). Towards an ai/ml-driven smo framework in o-ran: Scenarios, solutions, and challenges. arXiv.

[26]

Sharma "Misconfiguration in O-RAN: Analysis of the impact of AI/ML" Comput. Netw. (2024) 10.1016/j.comnet.2024.110455

[27]

Balakrishnan, R., Arvinte, M., Himayat, N., Nikopour, H., and Moustafa, H. (2024). Enhancing O-RAN Security: Evasion Attacks and Robust Defenses for Graph Reinforcement Learning-Based Connection Management. arXiv.

[28]

Amachaghi "A Survey for Intrusion Detection Systems in Open RAN" IEEE Access (2024) 10.1109/access.2024.3408690

[29]

Jiang "The road towards 6G: A comprehensive survey" IEEE Open J. Commun. Soc. (2021) 10.1109/ojcoms.2021.3057679

[30]

Kumar, R., Gupta, S., Wang, H., Kumari, C., and Korlam, S. (2023). From efficiency to sustainability: Exploring the potential of 6G for a greener future. Sustainability, 15. 10.3390/su152316387

[31]

Agarwal "Open RAN for 6G Networks: Architecture, Use Cases and Open Issues" IEEE Commun. Surv. Tutor. (2025) 10.1109/comst.2025.3562429

[32]

Awais, M., Ullah Khan, F., Zafar, M., Mudassar, M., Zaigham Zaheer, M., Mehmood Cheema, K., Kamran, M., and Jung, W. (2023). Towards Enabling Haptic Communications over 6G: Issues and Challenges. Electronics, 12. 10.3390/electronics12132955

[33]

da Costa, D., Zhao, Q., Chafii, M., Bader, F., and Debbah, M. (2023). 6G: Vision, Applications, and Challenges. Fundamentals of 6G Communications and Networking, Springer. 10.1007/978-3-031-37920-8_2

[34]

Ziegler "Security and trust in the 6G era" IEEE Access (2021) 10.1109/access.2021.3120143

[35]

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Cheng-Xiang Wang, Xiaohu You, Xiqi Gao et al.

IEEE Communications Surveys & Tutorials 2023 10.1109/comst.2023.3249835

[36]

Abdalla "ZTRAN: Prototyping Zero Trust Security xApps for Open Radio Access Network Deployments" IEEE Wirel. Commun. (2024) 10.1109/mwc.001.2300419

[37]

Houda, Z.A.E., Moudoud, H., and Khoukhi, L. (2024, January 20). Blockchain Meets O-RAN: A Decentralized Zero-Trust Framework for Secure and Resilient O-RAN in 6G and Beyond. Proceedings of the IEEE INFOCOM 2024—IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver, BC, Canada.

[38]

Jiang, H., Chang, H., Mukherjee, S., and Merwe, J. (2023, January 7–9). OZTrust: An O-RAN Zero-Trust Security System. Proceedings of the 2023 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Dresden, Germany. 10.1109/nfv-sdn59219.2023.10329620

[39]

Houda "Federated Deep Reinforcement Learning for Efficient Jamming Attack Mitigation in O-RAN" IEEE Trans. Veh. Technol. (2024) 10.1109/tvt.2024.3359998

[40]

Abouaomar "Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks" IEEE Commun. Mag. (2022) 10.1109/mcom.007.2200555

[41]

Kouchaki, M., Abdalla, A.S., and Marojevic, V. (2023, January 13–15). OpenAI dApp: An Open AI Platform for Distributed Federated Reinforcement Learning Apps in O-RAN. Proceedings of the 2023 IEEE Future Networks World Forum (FNWF), Baltimore, MD, USA. 10.1109/fnwf58287.2023.10520642

[42]

Rumesh, Y., Attanayaka, D., Porambage, P., Pinola, J., Groen, J., and Chowdhury, K.R. (2024, January 3–6). Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin. Proceedings of the 2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Antwerp, Belgium. 10.1109/eucnc/6gsummit60053.2024.10597083

[43]

Javed "Trustworthy Reputation for Federated Learning in O-RAN Using Blockchain and Smart Contracts" IEEE Open J. Commun. Soc. (2025) 10.1109/ojcoms.2025.3540159

[44]

Wu "HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks with Edge Computing Systems" IEEE Trans. Green Commun. Netw. (2024) 10.1109/tgcn.2024.3441532

[45]

Moudoud "Zero Trust Security Architecture for 6G Open Radio Access Networks (ORAN)" IEEE Netw. Lett. (2024) 10.1109/lnet.2024.3514357

[46]

Groen "Implementing and Evaluating Security in O-RAN: Interfaces, Intelligence, and Platforms" IEEE Netw. (2025) 10.1109/mnet.2024.3434419

[47]

Mimran, D., Bitton, R., Kfir, Y., Klevansky, E., Brodt, O., Lehmann, H., Elovici, Y., and Shabtai, A. (2022). Evaluating the security of open radio access networks. arXiv. 10.1016/j.cose.2022.102890

[48]

Chiejina, A., Kim, B., Chowdhury, K., and Shah, V.K. (2024, January 27–29). System-Level Analysis of Adversarial Attacks and Defenses on Intelligence in O-RAN Based Cellular Networks. Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks, Seoul, Republic of Korea. 10.1145/3643833.3656119

[49]

Sapavath, N.N., Kim, B., Chowdhury, K., and Shah, V.K. (2023, January 4–8). Experimental study of adversarial attacks on ML-based xApps in O-RAN. Proceedings of the GLOBECOM 2023—2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia. 10.1109/globecom54140.2023.10437125

[50]

Dayaratne, T., Vo, V., Lai, S., Abuadbba, S., Haydon, B., Suzuki, H., Yuan, X., and Rudolph, C. (2024). Exploiting and Securing ML Solutions in Near-RT RIC: A Perspective of an xApp. arXiv.

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Published: May 25, 2025
Vol/Issue: 17(6)
Pages: 233
License: View

Authors

M

Mohammed El-Hajj

Faculty of Computer Studies (FCS), Arab Open University (AOU), Beirut 1108, Lebanon; Department of Semantics, Cybersecurity & Services, University of Twente, 7522 Enschede, The Netherlands

Cite This Article

Mohammed El-Hajj (2025). Secure and Trustworthy Open Radio Access Network (O-RAN) Optimization: A Zero-Trust and Federated Learning Framework for 6G Networks. Future Internet, 17(6), 233. https://doi.org/10.3390/fi17060233

Secure and Trustworthy Open Radio Access Network (O-RAN) Optimization: A Zero-Trust and Federated Learning Framework for 6G Networks

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