Resilience Assessment of Multi-Layered Cyber-Physical Systems
Bibliographic record
Abstract
Thanks to technological advancements, critical infrastructures integrate many smart technologies and become highly connected to the cyber world. This is especially true for Cyber-Physical Systems (CPSs), which combine hardware and software components. Despite the advantages of smart infrastructures, e.g., sustainable energy usage, security, safety enhancement, and predictive algorithms using machine learning, they remain vulnerable to cyber threats and adversarial events such as cyber-attacks. This work focuses on the cyber resilience of CPSs. We propose a methodology leveraging knowledge graph modeling to increase the remediation potential of CPSs and to avoid critical failures that can occur due to cascading effects in complex architectures. We propose an approach based on multi-layered modeling applied to complex systems to achieve this objective. Indeed, a complex system can be considered an overlay of several layers. We use knowledge graphs to model a Secure Water Treatment System (SWaT) test bed subsystem. We conduct a resilience assessment analysis of several designs with a quantitative metric. This resilience analysis, applied to each layer of our models, is also used to highlight critical points, e.g., the key functions or components that are significant for completing a mission.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".