Resilient Distributed State Estimation for Nonlinear Cyber-Physical Systems with Sensor Networks under Cyberattacks
Bibliographic record
Abstract
This paper introduces a resilient distributed state estimation framework for nonlinear cyber-physical systems (CPS) using sensor networks under cyberattacks. Each sensor node integrates a state estimator and a cyberattack detector to enhance system robustness. The proposed approach combines the Distributed Hybrid Information Fusion (DHIF) technique with graph-theoretic principles for multi-step state estimation. False Data Injection (FDI) attacks on communication links are detected and isolated, with a resilient estimation strategy that adapts to changes in network topologies, ensuring that the system can recover from attack impacts. We show that distributed state estimation remains ultimately bounded, even with graph switching. A case study on an Unmanned Aerial Vehicle (UAV) navigating a sensor network with limited sensing coverage demonstrates the effectiveness of the proposed method in detecting cyberattacks, isolating compromised links, and recovering resilient state estimation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".