Mathematical and AI-Blockchain Integrated Framework for Strengthening Cybersecurity in National Critical Infrastructure
Bibliographic record
Abstract
This article examines the intersection between mathematical modeling, artificial intelligence (AI), and blockchain technology as a way of strengthening cybersecurity in national critical infrastructures (NCI). The increasing frequency and sophistication of cyber threats against vital infrastructures, such as electrical grids, healthcare information networks, and transportation infrastructures, creates the need to develop some innovative protective mechanisms. To solve these concerns, in the article the authors propose a hybrid framework that combines the AI-driven predictive analysis and the decentralized ledger capabilities of blockchain technology. Decentralized ledger technology (DLT) forms the backbone of secure and tamper-proof data management and machine learning algorithms are used to identify and predict emerging threats in real-time. By combining the immutability of the blockchain technology and the adaptive analytical capabilities of AI, this framework aims to improve the integrity of the data, maintain the privacy of it, and provide a fast-responding mechanism in the NCI environments. The paper outlines possible applications, lists the benefits that come with such applications, and addresses challenges inherent in the implementation of such an integrated system in order to provide a blueprint for future scholarly investigation and practical implementation.
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 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".