Optimal Linear Encryption Against Stealthy Attacks on Remote State Estimation
Bibliographic record
Abstract
Defending against malicious attacks has become increasingly important in various cyber-physical systems. This article presents an encryption-based countermeasure against stealthy attacks on remote state estimation. Smart sensors transmit data to a remote estimator through a wireless communication network, in which data packets can be intercepted and compromised by attackers. The remote end is equipped with a false data detector that monitors the system. To avoid being detected, the attack should follow the stealthiness constraint. A linear encryption scheme is proposed to reduce the influence of potential stealthy attacks. For arbitrary linear encryption, the worst-case linear attack that yields the largest estimation error is derived. Accordingly, the optimal linear encryption, which minimizes the worst-case estimation error, is designed based on the Stackelberg game analysis. The above optimal strategies are considered in both the complete and partial measurement information scenarios for the attacker. Moreover, the generalization to nonlinear encryption strategies is also discussed. Comparisons of attack and encryption strategies through numerical examples are provided to illustrate the theoretical results.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".