Blockchain for Securing CI/CD Pipeline: A Review on Tools, Frameworks, and Challenges
Bibliographic record
Abstract
Cloud security is an essential concern in cloud environments. Several sectors (e.g., business, healthcare, government, etc.) have witnessed recent cyber-attacks (e.g., HEalthEquity Data Breach, Capital One, Midnight Blizzard on Microsoft, etc.) on their computing systems. Our research focuses on security issues and conducts a literature review to address the integration of blockchain technology into Continuous Integration and Deployment (CI/CD) in the cloud environment pipeline to enhance security. The key idea rests on separating entities such as code, data, design, change history, etc., as “blocks” in the blockchain technology through “separation of concern”. Such modularisation should help contain any threats locally in the block concerned without affecting other blocks. Our initial focus is conducting a comprehensive review of existing literature to analyse and synthesise tools and technologies for integrating blockchain into the CI/CD pipeline on cloud services. We also highlight the challenges associated with current solutions. Through this research, we aim to identify and address the existing research gaps in this critical area.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".