Optimal Stealthy Deterministic Attack Strategies on Cyber-Physical Systems
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we study optimal stealthy attacks on both the actuator and output communication channels of cyber-physical control systems. Using a deterministic attack framework, we ensure that the attacks are difficult for innovation-based detectors to perceive. In order to determine the maximum performance degradation that the attacks may cause, a general optimization problem that can be solved numerically is formulated for a finite attack length. For nondivergent systems over an infinite horizon, the optimal constant and alternating attacks are derived analytically for any system configuration. Characteristics of a novel low-dimensional sinusoidal class of attacks are investigated and procedures for optimization are given. Furthermore, a condition is provided for constant and alternating attacks to be superior to most or all sinusoidal attacks. Finally, numerical simulations are used to demonstrate the theoretical results.
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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) | 0.001 | 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