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

Mohammed El-Hajj ()
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Mohammed El-Hajj: Faculty of Computer Studies (FCS), Arab Open University (AOU), Beirut 1108, Lebanon

Future Internet, 2025, vol. 17, issue 6, 1-60

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.

Keywords: O-RAN architecture; Zero-Trust Architecture (ZTA); Federated Learning (FL); 6G; energy efficiency optimization; adversarial Machine Learning (ML) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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