The rapid growth of AI-driven data centers and large-scale energy storage systems is increasing the reliance of power system operation on real-time measurement data and automated decision-making. However, many existing detection methods rely on statistical or data-driven analysis of measurements and can fail when attackers exploit the same data structure to craft stealthy perturbations. To illustrate this limitation, we demonstrate a blind False Data Injection Attack (FDIA) in which an Autoencoder learns the measurement manifold and generates perturbations aligned with the Jacobian null space, thereby allowing the attack to evade both residual-based baddata detectors and time-series anomaly detectors. To mitigate data-driven FDIAs which exploit the null space, we propose a topology-informed Cycle-Space Detector (CSD) that leverages the Cycle-Space of the network to impose structural constraints that enhance null space estimation. In addition, we prove that by using the Minimum Cycle Basis (MCB), the proposed CSD achieves the optimal generalization error for attack detection. By exploiting topology-derived cycle constraints rather than relying solely on numerical null space estimation, the proposed method does not require precise line parameters and improves the separation between normal and attacked measurements. Simulation results on IEEE 14-, 30-, 57-, and 118-bus systems demonstrate that the proposed method effectively detects data-driven FDIAs under realistic measurement noise.

翻译：人工智能驱动的数据中心和大规模储能系统的快速发展，使得电力系统运行越来越依赖实时测量数据和自动化决策。然而，许多现有检测方法依赖于对测量数据的统计或数据驱动分析，当攻击者利用相同的数据结构制造隐蔽扰动时，这些方法可能失效。为说明这一局限性，我们展示了一种盲虚假数据注入攻击（FDIA），其中自编码器学习测量流形并生成与雅可比零空间对齐的扰动，从而使攻击能够规避基于残差的坏数据检测器和时间序列异常检测器。为缓解利用零空间的数据驱动型FDIA，我们提出了一种拓扑感知的循环空间检测器（CSD），它利用网络的循环空间施加结构约束以增强零空间估计。此外，我们证明通过使用最小循环基（MCB），所提出的CSD在攻击检测中达到了最优泛化误差。该方法利用拓扑衍生的循环约束而非仅依赖数值零空间估计，因而无需精确线路参数，并改善了正常测量与受攻击测量之间的分离度。在IEEE 14、30、57和118节点系统上的仿真结果表明，所提方法在现实测量噪声条件下能够有效检测数据驱动型FDIA。