With a plethora of new connections, features, and services introduced, the 5th generation (5G) wireless technology reflects the development of mobile communication networks and is here to stay for the next decade. The multitude of services and technologies that 5G incorporates have made modern communication networks very complex and sophisticated in nature. This complexity along with the incorporation of Machine Learning (ML) and Artificial Intelligence (AI) provides the opportunity for the attackers to launch intelligent attacks against the network and network devices. These attacks often traverse undetected due to the lack of intelligent security mechanisms to counter these threats. Therefore, the implementation of real-time, proactive, and self-adaptive security mechanisms throughout the network would be an integral part of 5G as well as future communication systems. Therefore, large amounts of data collected from real networks will play an important role in the training of AI/ML models to identify and detect malicious content in network traffic. This work presents 5G-NIDD, a fully labeled dataset built on a functional 5G test network that can be used by those who develop and test AI/ML solutions. The work further analyses the collected data using common ML models and shows the achieved accuracy levels.
翻译:第五代(5G)无线技术引进了大量新的连接、特点和服务,反映了移动通信网络的发展,并留待下一个十年。5G所纳入的众多服务和技术使现代通信网络变得非常复杂和复杂。这种复杂性加上机器学习和人工智能的结合,使攻击者有机会对网络和网络装置发动智能攻击。这些攻击往往由于缺乏智能安全机制来应对这些威胁而未被发现。因此,在整个网络中实施实时、主动和自我适应的安全机制将是5G和未来通信系统的一个组成部分。因此,从实际网络收集的大量数据将在培训AI/ML模型中发挥重要作用,以查明和检测网络交通中的恶意内容。这项工作提出了5G-NIDDDD,这是一套完全贴上标签的数据集,它建在一个功能性5G测试网络上,开发并测试AI/ML解决方案的人可以使用。工作进一步分析利用通用ML模型收集的数据,并显示达到的准确度。