5G Core PFCP Intrusion Detection Dataset

George Amponis, Panagiotis I. Radoglou-Grammatikis, George Nakas, Sotirios K. Goudos, Vasileios Argyriou, Thomas Lagkas, Panagiotis G. Sarigiannidis

Abstract: The rapid evolution of the 5G environments introduces several benefits, such as faster data transfer speeds, lower latency and energy efficiency. However, this situation brings also critical cybersecurity issues, such as the complex and increased attack surface, privacy concerns and the security of the 5G core network functions. Therefore, it is evident that the role of intrusion detection mechanisms empowered with Artificial Intelligence (AI) models is crucial. Therefore, in this paper, we introduce a labelled security dataset called 5GC PFCP Intrusion Detection Dataset. This dataset includes a set of network flow statistics that can be used by AI detection models to recognise cyberattacks against the Packet Forwarding Control Protocol (PFCP). PFCP is used for the N4 interface between the Session Management Function (SMF) and the User Plane Function (UPF) in the 5G core. In particular, four PFCP attacks are investigated in this paper, including the relevant network traffic data in terms of pcap files and the Transmission Control Protocol (TCP)/Internet Protocol (IP) and application-layer statistics. This dataset is already publicly available in IEEE Dataport and Zenodo.