基于物理约束的电力系统故障定位后门攻击
Physics-Constrained Backdoor Attacks on Power System Fault Localization
摘要：
深度学习（DL）技术的进步有可能为现代电力系统中的许多复杂任务提供变革性的技术突破，这些任务受到越来越多的不确定性和非线性的影响。然而，DL的脆弱性还没有被彻底探讨在电力系统的各种物理约束下的任务。这项工作首次提出了一种新的物理约束后门中毒攻击，该攻击将不可检测的攻击信号嵌入到学习模型中，只有当遇到相应的信号时才执行攻击。本文阐述了实时故障线路定位应用程序的拟议攻击。此外，在68节点电力系统上的仿真结果表明，基于DL的故障线路定位方法对我们提出的攻击并不鲁棒，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。所提出的攻击流水线可以很容易地推广到其他电力系统的任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
