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- W4384517581 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." @default.
- W4384517581 created "2023-07-18" @default.
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- W4384517581 date "2023-06-28" @default.
- W4384517581 modified "2023-09-25" @default.
- W4384517581 title "5G Core PFCP Intrusion Detection Dataset" @default.
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- W4384517581 doi "https://doi.org/10.1109/mocast57943.2023.10176693" @default.
- W4384517581 hasPublicationYear "2023" @default.
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