Recent technological and architectural advancements in 5G networks have proven their worth as the deployment has started over the world. Key performance elevating factor from access to core network are softwareization, cloudification and virtualization of key enabling network functions. Along with the rapid evolution comes the risks, threats and vulnerabilities in the system for those who plan to exploit it. Therefore, ensuring fool proof end-to-end (E2E) security becomes a vital concern. Artificial intelligence (AI) and machine learning (ML) can play vital role in design, modelling and automation of efficient security protocols against diverse and wide range of threats. AI and ML has already proven their effectiveness in different fields for classification, identification and automation with higher accuracy. As 5G networks' primary selling point has been higher data rates and speed, it will be difficult to tackle wide range of threats from different points using typical/traditional protective measures. Therefore, AI and ML can play central role in protecting highly data-driven softwareized and virtualized network components. This article presents AI and ML driven applications for 5G network security, their implications and possible research directions. Also, an overview of key data collection points in 5G architecture for threat classification and anomaly detection are discussed.

中文翻译：

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5G网络安全中的人工智能与机器学习：机遇、优势和未来研究趋势

随着 5G 网络在全球范围内的部署，最近的技术和架构进步已经证明了它们的价值。接入核心网的关键性能提升因素是关键使能网络功能的软件化、云化和虚拟化。随着系统的快速演变，对于计划利用它的人来说，系统中存在风险、威胁和漏洞。因此，确保万无一失的端到端 (E2E) 安全性成为一个至关重要的问题。人工智能 (AI) 和机器学习 (ML) 可以在有效安全协议的设计、建模和自动化中发挥重要作用，以应对各种威胁。AI 和 ML 已经在不同领域证明了它们的有效性，可以更准确地进行分类、识别和自动化。作为 5G 网络的 主要卖点是更高的数据速率和速度，使用典型/传统保护措施很难解决来自不同点的广泛威胁。因此，人工智能和机器学习可以在保护高度数据驱动的软件化和虚拟化网络组件方面发挥核心作用。本文介绍了 AI 和 ML 驱动的 5G 网络安全应用、它们的影响和可能的研究方向。此外，还讨论了 5G 架构中用于威胁分类和异常检测的关键数据收集点的概述。本文介绍了 AI 和 ML 驱动的 5G 网络安全应用、它们的影响和可能的研究方向。此外，还讨论了 5G 架构中用于威胁分类和异常检测的关键数据收集点的概述。本文介绍了 AI 和 ML 驱动的 5G 网络安全应用、它们的影响和可能的研究方向。此外，还讨论了 5G 架构中用于威胁分类和异常检测的关键数据收集点的概述。