Distributed Model Predictive Consensus of MASs Against False Data Injection Attacks and Denial-of-Service Attacks
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 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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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 | 0.000 |
| 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