Blockchain implementation of public key infrastructure for Industry 5.0 applications
Bibliographic record
Abstract
Cyberattacks are quite prevalent in Industry 5.0 applications owing to the growing necessity of online communication in our day-to-day activities. X.509 Public-key Infrastructure (PKIX) is the most prominent concept to safeguard our online tasks. However, this concept is vulnerable to various security attacks, by which attackers adopt various policies to ruin the primary objective of this system in online communication. Consequently, focusing on PKIX vulnerabilities, numerous projects (e.g., Let's Encrypt and Google certificate transparency) have been initiated to safeguard online communication, which in turn assists Internet users against cyberattacks. On the other hand, blockchain is one of the key enabling technologies for Industry 5.0 applications from the perspective of ensuring security and privacy in a decentralized manner. In this chapter, we first provide a taxonomy of PKIX proposals in the context of blockchain technology while making a comparative study with other conventional PKIX proposals. Second, we define a list of evaluation metrics against some well-known cyberattacks and then compare different blockchain-based PKIX implementations in terms of these evaluation metrics and cyberattacks. Finally, we present some open challenges and future works in the context of Industry 5.0 applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".