论文标题
设计错误的数据注入攻击，以穿透基于AC的不良数据检测系统和FDI数据集生成
Designing False Data Injection attacks penetrating AC-based Bad Data Detection System and FDI Dataset generation
论文作者
论文摘要
传统电力系统向现代智能电网的演变为这一关键基础设施带来了许多新的网络安全挑战。最危险的网络安全威胁之一是虚假数据注入（FDI）攻击，尤其是当它能够完全绕过广泛部署的状态估计的不良数据检测器并中断电力系统的正常操作时。大多数模拟的外国直接投资攻击都是使用简化的线性DC模型设计的，而大多数行业标准状态估计系统基于非线性AC模型。在本文中，基于非线性AC模型提出了全面的外国直接投资攻击方案。使用行业标准包进行评估拟议设计方案的结果，提供了对九名公共汽车西方系统协调理事会（WSCC）的电力系统的案例研究。生成了公共外国直接投资数据集作为社区开发和评估该领域缺乏的新检测算法的测试集。通过基于物理能力法和统计分析的初步分析，评估和证明了FDI的隐身数据集质量。
The evolution of the traditional power system towards the modern smart grid has posed many new cybersecurity challenges to this critical infrastructure. One of the most dangerous cybersecurity threats is the False Data Injection (FDI) attack, especially when it is capable of completely bypassing the widely deployed Bad Data Detector of State Estimation and interrupting the normal operation of the power system. Most of the simulated FDI attacks are designed using simplified linearized DC model while most industry standard State Estimation systems are based on the nonlinear AC model. In this paper, a comprehensive FDI attack scheme is presented based on the nonlinear AC model. A case study of the nine-bus Western System Coordinated Council (WSCC)'s power system is provided, using an industry standard package to assess the outcomes of the proposed design scheme. A public FDI dataset is generated as a test set for the community to develop and evaluate new detection algorithms, which are lacking in the field. The FDI's stealthy quality of the dataset is assessed and proven through a preliminary analysis based on both physical power law and statistical analysis.
