The transition from 5G to 6G mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for CICIDS2017 datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.

翻译：从5G向6G移动网络的演进迫切需要网络自动化，以满足对高数据速率、超低延迟和技术融合日益增长的需求。近年来，由人工智能（AI）和机器学习（ML）驱动的零接触网络（ZTNs）被设计为以最少人工干预实现网络运营全生命周期的自动化，为提升5G/6G网络自动化水平提供了前景广阔的解决方案。然而，由于ZTNs高度依赖自动化，其实施也带来了对自主且鲁棒的网络安全解决方案的需求。AI/ML算法被广泛用于开发网络安全机制，但需要大量专业领域知识且面临模型漂移问题，这为开发自主网络安全措施带来了重大挑战。为此，本文提出一种针对物理层认证（PLA）和跨层入侵检测系统（CLIDS）的自动化安全框架，以应对多互联网协议层的安全问题。该框架采用漂移自适应的在线学习技术以及一种新颖的基于增强型连续减半（SH）的自动化机器学习（AutoML）方法，旨在为动态网络环境自动生成优化的ML模型。实验结果表明，所提框架在公开的射频（RF）指纹数据集和加拿大网络安全研究所的CICIDS2017数据集上均实现了高性能，证明了其在动态复杂网络环境中处理PLA和CLIDS任务的有效性。此外，本文探讨了5G/6G网络安全领域的开放挑战与研究方向。该框架代表了向完全自主且安全的6G网络迈出的重要一步，为未来网络自动化与网络安全领域的创新铺平了道路。