Cyber-Resilience Certification of Cyber-Physical Systems Subject to Impactful-Stealthy Cyber-Attacks
Bibliographic record
Abstract
Ensuring the cyber-resilience of control systems requires an analytical framework that systematically characterizes cyber-attacks, their stealthiness, and their impact on cyber-physical systems (CPS). In this work, we propose a novel control-theoretic framework for modeling impactful and stealthy cyber-attacks that induce instability in closed-loop dynamics despite integrity and privacy safeguards and avoid being detected. To this end, we leverage a time-reversed system formulation and recast the attack design problem as a robust stabilization problem, the solvability of which—depending on adversarial disruption and disclosure resources—certifies the system's vulnerability to such cyber-attacks. Additionally, we introduce a barrier function-based methodology to incorporate stealth constraints, enabling a quantitative analysis of the trade-offs between attack impact and detectability. The proposed framework thus provides a rigorous foundation for cyber-resilience certification, security standardization, and the development of attack detection and mitigation strategies in industrial control systems and CPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".