Data analysis and monitoring on smart grids are jeopardized by attacks on cyber-physical systems. False data injection attack (FDIA) is one of the classes of those attacks that target the smart measurement devices by injecting malicious data. The employment of machine learning techniques in the detection and localization of FDIA is proven to provide effective results. Training of such models requires centralized processing of sensitive user data that may not be plausible in a practical scenario. By employing federated learning for the detection of FDIA attacks, it is possible to train a model for the detection and localization of the attacks while preserving the privacy of sensitive user data. However, federated learning introduces new problems such as the personalization of the detectors in each node. In this paper, we propose a federated learning-based scheme combined with a hybrid deep neural network architecture that exploits the local correlations between the connected power buses by employing graph neural networks as well as the temporal patterns in the data by using LSTM layers. The proposed mechanism offers flexible and efficient training of an FDIA detector in a distributed setup while preserving the privacy of the clients. We validate the proposed architecture by extensive simulations on the IEEE 57, 118, and 300 bus systems and real electricity load data.

翻译：智能电网的数据分析与监控受到网络物理系统攻击的威胁。虚假数据注入攻击（FDIA）是一类针对智能测量设备注入恶意数据的攻击手段。机器学习技术在FDIA检测与定位中的应用已被证明能取得有效成果。此类模型的训练需要集中处理敏感用户数据，而这在实际场景中可能无法实现。通过将联邦学习应用于FDIA攻击检测，可在保护敏感用户数据隐私的前提下训练用于攻击检测与定位的模型。然而，联邦学习也引入了新问题，例如各节点检测器的个性化。本文提出一种基于联邦学习的方案，结合混合深度神经网络架构，利用图神经网络挖掘连接母线间的局部相关性，并通过LSTM层捕捉数据中的时序模式。所提机制在分布式场景下实现灵活高效的FDIA检测器训练，同时保护客户端隐私。我们在IEEE 57节点、118节点和300节点母线系统以及真实电力负荷数据上通过大量仿真验证了该架构的有效性。