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From Cycle Space to Cycle Manifold: Limits and Achievability of Blind False Data Injection Attacks

arxiv.org/abs/2609.10631

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Updated 3 h ago · first seen 11 Sept 2026

paper_01M294FPSXYN6JQXP0ZDJD7D1T

Published
11 Sept 2026
T1 · 3 h ago
arXiv
2609.10631
T1 · 3 h ago
Category
cs.CR
T1 · 3 h ago

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https://arxiv.org/abs/2609.10631currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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A false data injection attack (FDIA) can change the estimated grid state while evading a residual-based bad data detector (BDD). Existing blind attacks learn a low-rank measurement subspace, but this algebraic view does not state the physical grid constraints that make an attack stealthy or the minimum information needed to recover the complete attack space. Under the connected direct-current (DC) branch-flow model, we show that the residual-sensitive subspace of the noiseless orthogonal test is exactly the weighted cycle space. Its orthogonal complement is therefore the complete stealthy attack space, making weighted cycle-space knowledge both necessary and sufficient for complete blind FDIA. This space identifies the topology only up to 2-isomorphism and the relative cycle-edge parameters only up to one scale per biconnected component; bridge parameters are neither identified nor required. We then formulate a computationally unconstrained benchmark and a tractable measurement-only reconstruction method. Experiments on IEEE systems compare BDD bypass rate at a 95% nominal-acceptance threshold against state impact. As a compact alternating-current (AC) extension, we characterize feasible branch P/Q measurements by a cycle manifold and demonstrate topology-assisted manifold fitting and measurement generation on a graphics processing unit (GPU). In the lossless fixed-voltage small-angle limit, the normal space of the active-power slice reduces to the DC weighted cycle space.currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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crosscurrentcurrentarXiv (Atom API + RSS)T1highdeterministic

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2609.10631currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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Xin Li, Chenhan Xiao, Jonathan Cohen, Aviad Elyashar, Yang Weng, Rami PuziscurrentcurrentarXiv (Atom API + RSS)T1highdeterministic

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cs.CR, cs.LGcurrentcurrentarXiv (Atom API + RSS)T1highdeterministic

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https://arxiv.org/pdf/2609.10631currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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cs.CRcurrentcurrentarXiv (Atom API + RSS)T1highdeterministic

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11 Sept 2026currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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