分析O-RAN安全风险，用大模型等技术提升防御能力
Towards Secure Intelligent O-RAN Architecture: Vulnerabilities, Threats and Promising Technical Solutions using LLMs
- 用大模型、零信任等技术增强O-RAN架构安全性
- 实证移动目标防御提升动态切片准入控制效果
- 探索大模型解释性AI在安全防护中的应用价值
开放无线接入网（O-RAN）作为新一代智能网络架构，具备更高的灵活性与服务切片效率，但其安全性面临严峻挑战。本文深入分析O-RAN各层级潜在威胁，评估对机密性、完整性、可用性（CIA）三要素的影响。研究提出利用零信任、移动目标防御（MTD）、区块链及大语言模型（LLM）等技术强化安全防护。数值实验表明，MTD可有效提升深度强化学习在动态网络切片准入控制中的鲁棒性。同时，基于大模型的可解释AI（XAI）在系统安全保障中展现出潜力。
原文摘要 · Abstract (English)
The evolution of wireless communication systems will be fundamentally impacted by an open radio access network (O-RAN), a new concept defining an intelligent architecture with enhanced flexibility, openness, and the ability to slice services more efficiently. For all its promises, and like any technological advancement, O-RAN is not without risks that need to be carefully assessed and properly addressed to accelerate its wide adoption in future mobile networks. In this paper, we present an in-depth security analysis of the O-RAN architecture, discussing the potential threats that may arise in the different O-RAN architecture layers and their impact on the Confidentiality, Integrity, and Availability (CIA) triad. We also promote the potential of zero trust, Moving Target Defense (MTD), blockchain, and large language models(LLM) technologies in fortifying O-RAN's security posture. Furthermore, we numerically demonstrate the effectiveness of MTD in empowering robust deep reinforcement learning methods for dynamic network slice admission control in the O-RAN architecture. Moreover, we examine the effect of explainable AI (XAI) based on LLMs in securing the system.
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