Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems

2026-05-27

AI Summary

This work addresses the vulnerability of existing data-driven false data injection attack (FDIA) detection methods to stealthy perturbations crafted by exploiting the measurement manifold structure. To enhance robustness, the authors propose a novel detection framework that, for the first time, incorporates the cycle space structure of power grid topology. By leveraging a minimal cycle basis to constrain null space estimation, the method significantly improves the accuracy of identifying blind attacks generated by autoencoders. Notably, it operates without requiring precise line parameters and integrates graph theory, null space estimation, and residual-based time-series anomaly detection. Evaluated under realistic noise conditions on IEEE 14-, 30-, 57-, and 118-bus systems, the approach demonstrates substantially improved detection performance against data-driven attacks.

Abstract

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.

Problem

• False Data Injection Attack
• Autoencoder
• Power Systems
• Null Space
• Attack Detection

Innovation

• Cycle-Space Detector
• False Data Injection Attack
• Autoencoder
• Minimum Cycle Basis
• Topology-Informed Detection