Trends of Machine Learning, Cybersecurity and Big Data Analytics in Industry 4.0
Bibliographic record
Abstract
This research explores the integration of Machine Learning (ML), Cybersecurity, and Big Data Analytics (BDA) in advancing intelligent, secure, and sustainable industrial ecosystems within Industry 4.0. It assesses global research productivity, collaboration patterns, and the connection between intelligent automation, data-driven innovation, and cyber resilience. A PRISMA-based bibliometric review of 1,386 relevant publications from the Scopus database (2020-2025) was conducted, using Biblioshiny visualization tools to map key authors, institutions, countries, and emerging research clusters. Findings show a 7.09% annual growth in publications, reflecting a growing global focus on ML, BDA, and cybersecurity within Industry 4.0 ecosystems. The United States, China, and India were identified as major contributors, with strong cross-continental collaborations fostering innovation. Key research topics include deep learning, digital twins, and the Internet of Things (IoT), while emerging areas such as explainable AI, federated analytics, and edge computing are gaining attention. By mapping global research dynamics and identifying key contributors, this study highlights critical research gaps and offers practical insights for advancing interdisciplinary innovation, aimed at creating secure, intelligent, and sustainable industrial ecosystems in Industry 4.0.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.053 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".