Blockchain and Machine Learning: A Critical Review on Security
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML. Even though blockchain technology uses encryption to safeguard data, it is not completely reliable. Various elements, including the particular use case, the type of data, and legal constraints can determine whether it is suitable for keeping private and sensitive data. While there may be benefits, it is important to take into account possible hazards and abide by privacy and security laws. The blockchain itself is secure, but additional applications and layers are not. In terms of security, ML can aid in the development of blockchain applications. Therefore, a critical investigation is required to better understand the function of ML and blockchain in enhancing security. This study examines the current situation, evaluates the articles it contains, and presents an overview of the security issues. Despite their existing limitations, the papers included from 2012 to 2022 highlighted the importance of ML’s impact on blockchain security. ML and blockchain can enhance security, but challenges remain; advances such as federated learning and zero-knowledge proofs are important, and future research should focus on privacy and integration with other technologies.
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.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it