Robust Decentralized Control for Local Detection of Covert Cyberattacks in Interconnected Systems
Bibliographic record
Abstract
Interconnected systems consist of multiple subsystems coupled through physical and cyber connections. Within a subsystem, a covert cyberattack manipulates actuator commands to achieve a malicious objective while simultaneously manipulating sensor measurements to mimic nominal behavior, thereby remain undetected. Traditionally in the literature, approaches are proposed to detect and isolate such cyberattacks by using banks of observers, irrespective of any control scheme. In addition, the detection of a covert cyberattack within a subsystem as well as its isolation is feasible only by its neighbouring subsystems. In this paper, we propose an alternative approach, by incorporating a decentralized controller and a decentralized state-disturbance observer within each subsystem, which makes detection of covert cyberattacks feasible locally within each subsystem (i.e., local detection), thereby eliminating the reliance on neighbouring subsystems as well as the need for additional design complexity for isolation. The proposed approach is validated through numerical simulations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".