The popularity of 5G networks poses a huge challenge for malicious traffic detection technology. The reason for this is that as the use of 5G technology increases, so does the risk of malicious traffic activity on 5G networks. Malicious traffic activity in 5G networks not only has the potential to disrupt communication services, but also to compromise sensitive data. This can have serious consequences for individuals and organizations. In this paper, we first provide an in-depth study of 5G technology and 5G security. Next we analyze and discuss the latest malicious traffic detection under AI and their applicability to 5G networks, and compare the various traffic detection aspects addressed by SOTA. The SOTA in 5G traffic detection is also analyzed. Next, we propose seven criteria for traffic monitoring datasets to confirm their suitability for future traffic detection studies. Finally, we present three major issues that need to be addressed for traffic detection in 5G environment. The concept of incremental learning techniques is proposed and applied in the experiments, and the experimental results prove to be able to solve the three problems to some extent.

翻译：5G网络的普及对恶意流量检测技术构成了巨大挑战。其原因在于，随着5G技术使用的增加，5G网络上恶意流量活动的风险也随之上升。5G网络中的恶意流量活动不仅可能干扰通信服务，还可能导致敏感数据泄露，对个人和组织造成严重后果。本文首先对5G技术与5G安全进行了深入研究。其次，分析并讨论了当前人工智能框架下最新的恶意流量检测技术及其在5G网络中的适用性，并对比了现有最优方法（SOTA）所涉及的各类流量检测技术。同时，也分析了5G流量检测领域的SOTA现状。接着，我们提出了用于评估流量监测数据集适用于未来流量检测研究的七项标准。最后，我们提出了5G环境下流量检测需要解决的三个主要问题。本文引入增量学习技术概念并将其应用于实验，实验结果证明该技术能够在一定程度上解决上述三个问题。