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.
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This dataset was used for the paper "Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin" published at the EuCNC & 6G Summit, March 2024. Any use of this dataset which results in an academic publication or other publication which includes a bibliography should include a citation to our paper. Here is the reference for the work:
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@INPROCEEDINGS{10597083, author={Rumesh, Yasintha and Attanayaka, Dinaj and Porambage, Pawani and Pinola, Jarno and Groen, Joshua and Chowdhury, Kaushik}, booktitle={2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)}, title={Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin}, year={2024}, volume={}, number={}, pages={877-882}, keywords={Training;Machine learning algorithms;Federated learning;Emulation;Open RAN;Digital twins;Security;Open Radio Access Network;Network digital twin;Anomaly detection;Federated learning}, doi={10.1109/EuCNC/6GSummit60053.2024.10597083}}}