As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified.

中文翻译：

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智能电网中虚假数据注入攻击的检测：一种安全的联合深度学习方法


智能电网作为重要的信息物理系统（CPS），极易受到网络攻击。在各种类型的攻击中，虚假数据注入攻击（FDIA）被证明是最优先的网络相关问题之一，并且近年来受到越来越多的关注。然而，迄今为止，智能电网中 FDIA 检测中的隐私保护问题很少受到关注。受联邦学习的启发，本文结合 Transformer、联邦学习和 Paillier 密码系统，提出了一种基于安全联邦深度学习的 FDIA 检测方法。 Transformer 作为部署在边缘节点的检测器，利用其多头自注意力机制深入研究各个电量之间的联系。通过使用联邦学习框架，我们的方法利用来自所有节点的数据来协作训练检测模型，同时通过在训练期间将数据保留在本地来保护数据隐私。为了提高联邦学习的安全性，将Paillier密码体制与联邦学习相结合，设计了一种安全的联邦学习方案。通过在IEEE 14总线和118总线测试系统上的大量实验，验证了该方法的有效性和优越性。