  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.
