Towards privacy-preserving anomaly-based intrusion detection in energy communities

Energy communities consist of decentralized energy production, storage, consumption, and distribution and are gaining traction in modern power systems. However, these communities may increase the vulnerability of the grid to cyber threats. We propose an anomaly-based intrusion detection system to enhance the security of energy communities. The system leverages LSTM autoencoders to detect deviations from normal operational patterns in order to identify anomalies induced by attacks or faults. Operational data for training and evaluation are derived from a Simulink-based model of an energy community. The results show that the autoencoder-based intrusion detection system achieves good detection performance across multiple attack scenarios, up to 0.9270 and 0.9735 in precision and recall respectively. We also demonstrate potential for real-world application of the system by training a federated model that enables distributed intrusion detection while preserving data privacy.