Optimal defence strategy for chemical industry park cyber–physical systems: An improved logit dynamic evolutionary game‐theoretic method
Bibliographic record
Abstract
Abstract With the development of Industry 4.0, cyber–physical systems have been widely applied in chemical industry parks to promote intelligent production. However, the cyber–physical systems in chemical industry parks are vulnerable to emerging cascading risks such as cyber attacks, which may lead to severe accidents. Effective security defence strategies are particularly important to ensure the stable operation of the systems. Therefore, this study proposes an optimal defence strategy generation method for the chemical industry park cyber–physical systems based on logit dynamics and evolutionary game theory. Firstly, this method constructs an attack–defence game model based on the improved logit dynamics and evolutionary game theory. By introducing the rationality degree (RD) and the prevention‐control level (PCL) factor into the original logit dynamic evolution equation, it is used to explain and analyze the dynamic choice process and mechanism of strategic choices. Secondly, a novel attack–defence utility quantification method based on the characteristics of vulnerabilities is proposed, which innovatively combines the confidence level of attack operations and the impact of vulnerabilities to quantify the utility. Finally, a real experimental platform is built for case study, and four scenarios are established to conduct numerical analysis on the game evolution model. Furthermore, the influence of the RD and the PCL factor on the evolution process of attack–defence strategies is analysed. The experimental results verify the accuracy and effectiveness of the proposed method in generating optimal defence strategies.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".