Worst-Case Stealthy Attacks on Stochastic Event-Based State Estimation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article considers the worst-case stealthy attack strategies on stochastic event-based state estimation. Smart sensors equipped with local event-triggered Kalman filters are used to transmit innovations. Contrary to classic Kalman filters, the transmitted innovation screened by the stochastic decision rule does not follow a Gaussian distribution. A type of distribution called complete Gaussian crater is defined and analyzed, which is essential for designing stealthy attacks. The evolution of the estimation error covariance under attacks is obtained. Stealthy attacks that yield the greatest estimation errors under constraints on transmission rates and probability distributions are obtained and analyzed. The system performance degradation caused by different attacks is evaluated via simulations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it