Graph Analytics for Cyber-Physical System Resilience Quantification
Bibliographic record
Abstract
Critical infrastructures integrate a wide range of smart technologies and become highly connected to the cyber world. This is especially true for Cyber-Physical Systems (CPSs), which integrate hardware and software components. Despite the advantages of smart infrastructures, they remain vulnerable to cyberattacks. This work focuses on the cyber resilience of CPSs. We propose a methodology based on knowledge graph modeling and graph analytics to quantify the resilience potential of complex systems by using a multilayered model based on knowledge graphs. Our methodology also allows us to identify critical points. These critical points are components or functions of an architecture that can generate critical failures if attacked. Thus, identifying them can help enhance resilience and avoid cascading effects. We use the SWaT (Secure Water Treatment) testbed as a use case to achieve this objective. This system mimics the actual behavior of a water treatment station in Singapore. We model three resilient designs of SWaT according to our multilayered model. We conduct a resilience assessment based on three relevant metrics used in graph analytics. We compare the results obtained with each metric and discuss their accuracy in identifying critical points. We perform an experimentation analysis based on the knowledge gained by a cyber adversary about the system architecture. We show that the most resilient SWaT design has the necessary potential to bounce back and absorb the attacks. We discuss our results and conclude this work by providing further research axes.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".