Dataset for Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin

We consider an experimental architecture where one or more gNBs (i.e., a combination of RU, DU and CU) with an E2 interface connect to one near-RT RIC. Each gNB is able to support multiple traffic slices. In our experiment, we choose to use three broad 5G slices: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra reliable low latency communication (URLLC). UEs are assigned to the appropriate traffic slices. The gNB records a wide range of Key Performance Indicators (KPIs) and periodically reports these KPIs to an xApp in the near-RT RIC. For each traffic slice, we generate both normal traffic and attack traffic that comprises anomalies.