论文标题
物理受限的后门攻击电源系统故障本地化
Physics-Constrained Backdoor Attacks on Power System Fault Localization
论文作者
论文摘要
深度学习（DL）技术的进步有可能为现代电力系统的众多复杂任务提供变革性的技术突破，这些任务因不确定性和非线性的增加而受苦。但是，在各种物理限制下，在电力系统任务中尚未对DL的脆弱性进行彻底探索。这项工作首次提出了一种新颖的物理受限的后门中毒攻击，该攻击将无法检测到的攻击信号嵌入到学习的模型中，并且仅在遇到相应信号时才执行攻击。本文说明了对实时故障线定位应用程序的拟议攻击。此外，对68总线电源系统的仿真结果表明，基于DL的故障线定位方法对我们提出的攻击并不强大，这表明后门中毒攻击对电力系统中的DL实现构成了真正的威胁。提议的攻击管道可以轻松地将其推广到其他电力系统任务。
The advances in deep learning (DL) techniques have the potential to deliver transformative technological breakthroughs to numerous complex tasks in modern power systems that suffer from increasing uncertainty and nonlinearity. However, the vulnerability of DL has yet to be thoroughly explored in power system tasks under various physical constraints. This work, for the first time, proposes a novel physics-constrained backdoor poisoning attack, which embeds the undetectable attack signal into the learned model and only performs the attack when it encounters the corresponding signal. The paper illustrates the proposed attack on the real-time fault line localization application. Furthermore, the simulation results on the 68-bus power system demonstrate that DL-based fault line localization methods are not robust to our proposed attack, indicating that backdoor poisoning attacks pose real threats to DL implementations in power systems. The proposed attack pipeline can be easily generalized to other power system tasks.
