Integrity Attacks on Remote Estimation Under Sequential Detection
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 note investigates the worst-case performance of multi-sensor remote estimation compromised by integrity attacks. In addition to the residual-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\chi ^{2}$</tex-math></inline-formula> detector commonly deployed for unreliable sensors, the reliability of certain sensors allows for a second detector to be applied sequentially. This enhanced detection mechanism imposes stricter stealthiness constraints on integrity attacks, thereby increasing the complexity of vulnerability analysis. To characterize the maximum degradation in estimation performance, we propose a novel attack pattern that is constructed based on an efficient utilization of available information. The resulting worst-case performance and corresponding optimal attacks can be derived in closed form. The optimality of the proposed strategies among all feasible attacks is confirmed by analyzing the structure of the associated optimization problem. To improve practical applicability, we further consider attacks without access to reliable sensor data. By specifying the feasibility condition and deriving the optimal attacks, the vulnerability is more clearly revealed. Finally, numerical simulations are provided to validate the theoretical findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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