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. Link to paper https://arxiv.org/abs/2212.01298

2022