Optimal Deception Attacks Against Remote State Estimation: An Information-Based Approach
Bibliographic record
Abstract
This paper studies the problem of deception attacks against remote state estimation from an information perspective. The Kullback–Leibler divergence between the compromised innovation and nominal one is utilized as the stealthiness measure. Without presupposing a linear attack model, the optimal attack policy that can cause maximum performance loss and deceive the false data detector is derived. For both attacks with strict and relaxed stealthiness, the optimal compromised innovation, which is shown to be generated by a linear time-varying system, can be determined with two steps. First, the minimum mean-square error (MMSE) estimate of the prediction error is obtained using attackers' available information. Then, the faked innovation is designed as a linear transformation of the MMSE estimate. Within a unified framework, this separation principle enables handling more general attack scenarios, where the attacker may obtain more (or less) measurement data than the remote estimator. The optimality of the information-based strategy is verified by theoretical analysis, numerical examples, as well as comparative studies with existing methods.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".