From Cycle Space to Cycle Manifold: Limits and Achievability of Blind False Data Injection Attacks
- URL: http://arxiv.org/abs/2609.10631v1
- Date: Wed, 09 Sep 2026 07:19:07 GMT
- Title: From Cycle Space to Cycle Manifold: Limits and Achievability of Blind False Data Injection Attacks
- Abstract summary: A false data injection attack (FDIA) can change the estimated grid state while evading a residual-based bad data detector (BDD)<n>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.<n>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.
- Score: 11.81457841153631
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Certifying Plans under Model Mismatch: A Trilemma for Reachability from Scarce Data [5.455772242709494]
Sim-to-real policies are designed under nominal dynamics, but target-system trials may yield only a few isolated one-step transitions.<n>We study pre-execution certification of a fixed control sequence, such as an action chunk produced by a learned policy.
arXiv Detail & Related papers (2026-08-03T16:28:26Z) - Machine Learning-Based Reconstruction for Resistive Silicon Sensors [53.365453214937496]
We study machine-learning-based reconstruction and compression for resistive silicon sensors.<n>We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information.<n>We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction.
arXiv Detail & Related papers (2026-07-13T14:03:26Z) - Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks [11.81457841153631]
False data injection attacks (FDIAs) can still cause large deviations in power system state estimation when the injected signals align with the pseudo-null space of the system model.<n>Existing model- and data-driven detectors may fail to identify such low-magnitude but high-impact attacks because residual tests ignore changes hidden in the pseudo-null space.<n>This paper proposes Physically Consistent Null Space Alignment (PCNSA), a framework that detects stealthy FDIAs by preserving, through preprocessing, the geometric correspondence between the physical null space and the measurement-derived pseudo-null space.
arXiv Detail & Related papers (2026-06-07T06:39:09Z) - Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems [11.81457841153631]
AI-driven data centers and large-scale energy storage systems are increasing the reliance of power system operation on real-time measurement data and automated decision-making.<n>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.<n>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.<n>To mitigate data-driven FDIAs which exploit the null space, we propose a topology-informed Cycle-Space Detector (CSD) that
arXiv Detail & Related papers (2026-05-27T17:36:18Z) - Physics from Video: Identifiability of Time-Invariant Second-Order ODEs under Minimal Trajectory Conditions [60.2784641643737]
Bridging the gap between visual realism and physical understanding is a core challenge for video-based world models.<n>We study the structural identifiability of continuous-time physical laws from raw pixels.<n>We prove that a level-set slope-coverage condition ensures the learned latent space is locally affine to the true physical state.
arXiv Detail & Related papers (2026-05-27T13:22:29Z) - High-Dimensional Latents Should Be Diagnosed Through Phase Structure [56.362776482614976]
We study autoencoder and variational-autoencoder latent spaces through the lens of spin-glass theory.<n>We formalize a latent-space spin-glass dictionary for a fixed decoder.<n>We show that driving the latent system toward the edge-of-stability of the topological trivialization regime has concrete downstream consequences.
arXiv Detail & Related papers (2026-05-23T06:17:23Z) - SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere [90.85001795656633]
Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos.<n>We propose SphereVAD, a fully training-free, zero-shot VAD framework that recasts anomaly discrimination as von Mises-Fisher (vMF) likelihood-ratio geodesic inference on the unit hypersphere.
arXiv Detail & Related papers (2026-05-08T16:57:38Z) - LACE: Loss-Adaptive Capacity Expansion for Continual Learning [0.0]
LACE (Loss-Adaptive Capacity Expansion) is an online mechanism that expands a model's representational capacity during training.<n>LACE requires no labels, no replay buffers, and no external controllers, making it suitable for on-device continual learning under resource constraints.
arXiv Detail & Related papers (2026-03-30T15:58:33Z) - Robust Sequential Tracking via Bounded Information Geometry and Non-Parametric Field Actions [0.0]
Empirical benchmarks across three domains--LiDAR maneuvering target tracking, high-frequency cryptocurrency order flow, and quantum state tomography--demonstrate that this information geometry analytically truncates outliers.
arXiv Detail & Related papers (2026-03-13T21:40:44Z) - Phase-space entropy at acquisition reflects downstream learnability [54.4100065023873]
We propose an acquisition-level scalar $S_mathcal B$ based on instrument-resolved phase space.<n>We show theoretically that (S_mathcal B) correctly identifies the phase-space coherence of periodic sampling.<n>$|S_mathcal B|$ consistently ranks sampling geometries and predicts downstream reconstruction/recognition difficulty emphwithout training.
arXiv Detail & Related papers (2025-12-22T10:03:51Z) - DAAIN: Detection of Anomalous and Adversarial Input using Normalizing
Flows [52.31831255787147]
We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA)
Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution.
Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
arXiv Detail & Related papers (2021-05-30T22:07:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.