5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network

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.

5G-NIDD contains data extracted from a 5G testbed. The testbed is attached to 5G Test Network in University of Oulu, Finland. The data are extracted from tow base stations, each having an attacker node, several benign 5G users. The attacker nodes attack the server deployed in 5GTN MEC environment. The attack scenarios include DoS attacks and port scans. Under DoS attacks, the dataset contains ICMP Flood, UDP Flood, SYN Flood, HTTP Flood, and Slowrate DoS. Under port scans, the dataset contains SYN Scan, TCP Connect Scan, and UDP Scan.

The dataset files are available in different formats. These files belong to a series of post-processing steps from network capture (pcapng) to encoded data (csv) ready to feed ML algorithms.