Important-data-based attack design and resilient remote estimation for recurrent neural networks
Bibliographic record
Abstract
In this paper, an attack-defense framework is proposed for the remote H ∞ state estimation of delayed recurrent neural networks (RNNs). Firstly, an important-data-based (IDB) attack strategy is constructed, which can identify the important packets that play essential roles in the estimation and selectively attack them based on their importance degree from the perspective of the attackers. By targeting the important packets, larger attack damages can be achieved. Then, a resilient state estimator that can resist IDB attacks is developed from the defenders' point of view. Notably, some unrealistic assumptions (e.g., the attacker knowing the system structure and full parameters, the defender knowing the attack rate/parameters) are removed, which makes the proposed method easy to implement. At last, simulation results are presented to show the larger destructive effect of the constructed IDB attack and the efficiency of the proposed resilient H ∞ state estimator .
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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 | 0.001 | 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 | 0.002 |
| 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".