TY - GEN
T1 - 5G core PFCP intrusion detection dataset
AU - Amponis, George
AU - Radoglou-Grammatikis, Panagiotis
AU - Nakas, George
AU - Goudos, Sotirios
AU - Argyriou, Vasileios
AU - Lagkas, Thomas
AU - Sarigiannidis, Panagiotis
N1 - Organising Body: IEEE
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Computer science and informatics
U2 - 10.1109/MOCAST57943.2023.10176693
DO - 10.1109/MOCAST57943.2023.10176693
M3 - Conference contribution
SN - 9798350321081
T3 - International Conference on Modern Circuits and Systems Technologies (MOCAST)
BT - 2023 12th International Conference on Modern Circuits and Systems Technologies (MOCAST)
PB - Institute of Electrical and Electronics Engineers
T2 - 12th International Conference on Modern Circuits and Systems Technologies (MOCAST)
Y2 - 28 June 2023 through 30 June 2023
ER -