论文标题
数据驱动的虚假数据注入电源攻击：随机矩阵方法
Data-Driven False Data Injection Attacks Against Power Grids: A Random Matrix Approach
论文作者
论文摘要
我们解决了构建虚假数据注入（FDI）攻击的问题，该攻击可以绕过电网的不良数据检测器（BDD）。假定攻击者只能访问功率流量测量数据痕迹（在有限的时间内收集），并且没有其他关于网格的先验知识。现有相关算法是在攻击者可以访问长期（渐近无限）时间内收集的测量的假设下提出的，这可能是不现实的。我们表明，当攻击者仅具有有限数量的数据样本时，这些方法的表现不佳。我们设计了一种增强的算法，以构建有限测量值的FDI攻击向量，但仍可以以很高的概率绕过BDD。算法设计取决于随机矩阵理论的结果。此外，我们表征了攻击的BDD-BYPASS概率与其稀疏性之间的重要权衡，这影响了必须实现的攻击的空间范围。使用从MATPOPER模拟器和基准IEEE总线系统收集的数据轨迹的大量模拟验证了我们的发现。
We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic. We show that these approaches do not perform well when the attacker has a limited number of data samples only. We design an enhanced algorithm to construct FDI attack vectors in the face of limited measurements that can nevertheless bypass the BDD with high probability. The algorithm design is guided by results from random matrix theory. Furthermore, we characterize an important trade-off between the attack's BDD-bypass probability and its sparsity, which affects the spatial extent of the attack that must be achieved. Extensive simulations using data traces collected from the MATPOWER simulator and benchmark IEEE bus systems validate our findings.
