Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks. This article introduces the joint adversarial example and FDIAs (AFDIAs) to explore various attack scenarios for state estimation in power systems. Considering that perturbations added directly to measurements are likely to be detected by BDDs, our proposed method of adding perturbations to state variables can guarantee that the attack is stealthy to BDDs. Then, malicious data that are stealthy to both BDDs and deep learning-based detectors can be generated. Theoretical and experimental results show that our proposed state-perturbation-based AFDIA method (S-AFDIA) can carry out attacks stealthy to both conventional BDDs and deep learning-based detectors, while our proposed measurement-perturbation-based adversarial FDIA method (M-AFDIA) succeeds if only deep learning-based detectors are used. The comparative experiments show that our proposed methods provide better performance than state-of-the-art methods. Besides, the ultimate effect of attacks can also be optimized using the proposed joint attack methods.

中文翻译：

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电力系统状态估计的联合对抗示例和虚假数据注入攻击


尽管使用不良数据检测器 (BDD) 进行状态估计是电力系统中采用的关键过程，但该检测器容易受到虚假数据注入攻击 (FDIA)。人们已经提出了大量的深度学习方法来检测此类攻击。然而，深度神经网络很容易受到对抗性攻击或对抗性示例的影响，即使在训练有素的网络中，输入的微小变化也可能导致相应输出的急剧变化。本文介绍了联合对抗示例和FDIA（AFDIA）来探索电力系统状态估计的各种攻击场景。考虑到直接添加到测量中的扰动很可能被 BDD 检测到，我们提出的向状态变量添加扰动的方法可以保证攻击对于 BDD 来说是隐秘的。然后，可以生成对 BDD 和基于深度学习的检测器来说都是隐秘的恶意数据。理论和实验结果表明，我们提出的基于状态扰动的 AFDIA 方法（S-AFDIA）可以对传统 BDD 和基于深度学习的检测器进行隐形攻击，而我们提出的基于测量扰动的对抗 FDIA 方法（M-如果仅使用基于深度学习的检测器，AFDIA）就会成功。比较实验表明，我们提出的方法比最先进的方法提供了更好的性能。此外，还可以使用所提出的联合攻击方法来优化攻击的最终效果。