Cyberattack Detection for a Class of Nonlinear Multiagent Systems Using Set-Membership Fuzzy Filtering
Bibliographic record
Abstract
This article studies cyberattack detection in discrete-time leader-following nonlinear multiagent systems subject to unknown but bounded system noises. The Takagi–Sugeno fuzzy model is used to approximate the nonlinear systems over the true value of the state. A new method is developed for simultaneous distributed cyberattack detection and leader-following consensus control. The method is based on a fuzzy set-membership filtering consisting of two steps: a prediction and a measurement update. An estimation ellipsoid set is found by updating the prediction ellipsoid set with the current sensor measurement data. Two criteria are provided to detect cyberattacks that inject malicious signals into sensor data, communication channel signals, and control signals based on the intersection between the ellipsoid sets. If there is no intersection between the prediction set and the estimation set of an agent at the current time instant, then a cyberattack on its sensors is declared. Control or communication signals of an agent are under a cyberattack if their prediction sets have no intersection with the estimation sets updated at the previous time instant. Recursive algorithms are proposed for solving the consensus protocol and calculating the two ellipsoid sets. Two cyberattack recovery mechanisms are introduced.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".