The Vulnerability Analysis of Remote Estimation With Batch-Data Detectors Against Integrity Attacks
Bibliographic record
Abstract
The vulnerability analysis of remote state estimation with batch-data detectors is investigated in this article. The considered scenario is that sensors measure the process state and transmit measurements to the remote end via wireless networks, where the yielded innovation may be altered by an adversary in an affine form. At the remote end, a detector utilizing batch statistics is deployed to detect anomalies. In this setup, finding the worst-case estimation performance degradation during a detection interval is formulated as a nonconvex optimization problem on the Stiefel manifold with linear equation constraints, which is generally hard to tackle. Such a problem is addressed by introducing new optimization variables, and structural expressions of attack strategies in worst-case are proposed. Then, by means of Riemannian optimization tools, we provide additional properties for such attack strategies in a scalar sensor by solving a boundary trust region subproblem. Furthermore, analytical attack strategies in worst-case are derived under the case where a sequence of contaminated innovation covariances has been determined. A necessary condition of the existence of attack strategies in worst-case for the attacker with symmetric attack parameter matrices is also presented. Finally, two numerical examples in multisensor and a scalar sensor are conducted to demonstrate the validity of results developed.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 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".