Deep Neural Networks have proven to be highly accurate at a variety of tasks in recent years. The benefits of Deep Neural Networks have also been embraced in power grids to detect False Data Injection Attacks (FDIA) while conducting critical tasks like state estimation. However, the vulnerabilities of DNNs along with the distinct infrastructure of cyber-physical-system (CPS) can favor the attackers to bypass the detection mechanism. Moreover, the divergent nature of CPS engenders limitations to the conventional defense mechanisms for False Data Injection Attacks. In this paper, we propose a DNN framework with additional layer which utilizes randomization to mitigate the adversarial effect by padding the inputs. The primary advantage of our method is when deployed to a DNN model it has trivial impact on the models performance even with larger padding sizes. We demonstrate the favorable outcome of the framework through simulation using the IEEE 14-bus, 30-bus, 118-bus and 300-bus systems. Furthermore to justify the framework we select attack techniques that generate subtle adversarial examples that can bypass the detection mechanism effortlessly.

翻译：深度神经网络近年来在各类任务中展现出极高的准确性。其优势已被引入电力系统，在执行状态估计等关键任务时检测虚假数据注入攻击。然而，深度神经网络的脆弱性以及信息物理系统独特的基础设施可能助长攻击者绕过检测机制。此外，信息物理系统的异质性使得针对虚假数据注入攻击的传统防御机制存在局限性。本文提出了一种带有附加层的深度神经网络框架，通过填充输入引入随机化以缓解对抗效应。该方法的主要优势在于部署到深度神经网络模型后，即使采用较大的填充尺寸，对模型性能的影响也微乎其微。我们通过IEEE 14节点、30节点、118节点和300节点系统的仿真，展示了该框架的优越效果。此外，为验证框架的有效性，我们选取了能够生成可轻松绕过检测机制的隐蔽对抗样本的攻击技术。