Blockchain technologies and their application in security software development
Bibliographic record
Abstract
Current factors like the rising frequency of cyber threats and vulnerabilities on centralized platforms indicate the inefficiency of conventional network security frameworks, leading to new solutions such as the blockchain. This review systematically reviews developments of blockchain technologies in the context of security software (2021-2023) to evaluate its efficacy and challenges and explore the future potential. 77 peer-reviewed papers from ScienceDirect, IEEE Xplore and Scopus; adopting the PRISMA guideline, records were screened down from 1,532 to 77. Empirical evaluations (35%), case studies (28%), and theoretical frameworks (37%) using Joanna Briggs Institute tools and the Newcastle-Ottawa Scale were used in mitigating bias. Our results show that blockchain has strengths that add to data integrity (89% of studies) and security of the Internet of Things (IoT) ecosystem (28 studies) and supply chains (15 studies). Nevertheless, blockchain-based authentication has reduced latency by 284% (342 ± 112 ms) compared to a traditional system and has tradeoffs with scalability and performance. Research is skewed towards finance (47%), missing healthcare (9%), and critical infrastructure (6%). It does not include sufficient interoperability standards, post-quantum cryptographic validation, etc. The adaptive regulations are urged for policy implications for editable blockchains and hybrid Artificial Intelligence (AI) blockchain architectures. Interoperability should be taken care of by cross-chain protocols, scalability trilemmas and real-world adversarial testing must be addressed by the researchers and practitioners must put priority on scalability. This review, in its totality, brings out the singular role of blockchain in complementing the existing security solutions instead of replacing them. It calls for cross-disciplinary involvement and partnership in harnessing technical innovation in a regulatory framework to tackle cybersecurity threats through outsider and insider security approaches.
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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".