Distributed Model Predictive Consensus of MASs Against False Data Injection Attacks and Denial-of-Service Attacks
Bibliographic record
Abstract
This article provides a secure distributed output feedback model predictive control (DOFMPC) solution for the leader-following consensus problems of homogeneous linear disturbed multiagent systems against multiple cyber attacks. The false data injection (FDI) attacks on the sensor-controller communication channel and denial-of-service (DoS) attacks on the controller-actuator communication channel occur simultaneously. To defend against dual-channel multiple attacks, we propose a secure DOFMPC scheme consisting of three modules. Firstly, a robust multivariate observer is built to separate FDI attacks from uncompromised states. Secondly, a distributed output feedback model predictive controller is designed to generate effective control sequences. Thirdly, a buffered actuator is added to realize adequate compensation to defend against DoS attacks. The proposed secure DOFMPC scheme ensures the recursive feasibility of the formulated optimization control problem and achieves the leader-following consensus of the multiagent system. Finally, an illustrative example is presented to demonstrate the secure performance of the proposed DOFMPC scheme.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".