5G technology, increasingly present in sectors such as industrial automation and smart cities, represents a major advance in connectivity, speed, and latency. However, these environments can also be affected by interference, such as jamming attacks, which can physically deny service by overwhelming the radio spectrum. This work presents a labeled dataset generated in a controlled private 5G SA (standalone) network environment, including both legitimate traffic and traffic affected by jamming. To this end, an experimental scenario was prepared using real devices and a dedicated system to perform the interference attack. The analysis focuses on key protocols such as NGAP and GTP, with the aim of providing a dataset for the development of AI-based intrusion detection systems in 5G environments, adapted to the specific characteristics of these networks.