The advancements in the field of telecommunications have resulted in an increasing demand for robust, high-speed, and secure connections between User Equipment (UE) instances and the Data Network (DN). The implementation of the newly defined 3rd Generation Partnership Project 3GPP (3GPP) network architecture in the 5G Core (5GC) represents a significant leap towards fulfilling these demands. This architecture promises faster connectivity, low latency, higher data transfer rates, and improved network reliability. 5GC has been designed to support a wide range of critical Next Generation Internet of Things (NG-IoT) and industrial use cases that require reliable end-to-end communication services. However, this evolution raises severe security issues. In the context of the SANCUS1 project, a set of cyberattacks were investigated and emulated by K3Y against the Packet Forwarding Control Protocol (PFCP) between the Session Management Function (SMF) and the User Plane Function (UPF). Based on these attacks, an intrusion detection dataset was generated: 5GC PFCP Intrusion Detection Dataset that can support the development of Artificial Intelligence (AI)-powered Intrusion Detection Systems (IDS) that use Machine Learning (ML) and Deep Learning (DL) techniques. The goal of this report is to describe this dataset.