AI RESEARCH
Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems
arXiv CS.LG
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ArXi:2605.28912v1 Announce Type: new 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.