5G网络安全中的人工智能和机器学习：机遇、优势和未来研究趋势
Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends
摘要：
5G网络最近的技术和架构进步已经证明了它们的价值，因为部署已经在世界各地开始。从接入到核心网络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。随着网络的快速发展，系统中的风险、威胁和漏洞也随之而来。因此，确保端到端（E2 E）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）可以在针对各种各样的威胁的高效安全协议的设计、建模和自动化方面发挥至关重要的作用。AI和ML已经在不同领域证明了它们在分类、识别和自动化方面的有效性，并且具有更高的准确性。由于5G网络的主要卖点是更高的数据率和速度，因此很难使用典型/传统的保护措施来应对来自不同点的各种威胁。因此，AI和ML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，它们的含义和可能的研究方向。此外，概述了5G架构中用于威胁分类和异常检测的关键数据汇集点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
