Meta-Review of Recent and Landmark Honeypot Research and Surveys
Bibliographic record
Abstract
The growing interest in Honeypots has resulted in increased research, and consequently, a large number of research surveys and/or reviews. Most Honeypot surveys and/or reviews focus on specific and narrow Honeypot research areas. This study aims at exploring and presenting advances and trends in Honeypot’s research and development areas. To this end, a systematic methodology and meta-review analysis were applied to the selection, evaluation, and qualitative examination of the most influential Honeypot surveys and/or reviews available in scientific bibliographic databases. A total of 188 papers have been evaluated and 22 research papers are found by this study to have a higher impact. The findings of the study suggest that the Honeypot survey and/or review papers of considerable relevance to the research community were mostly published in 2018, by IEEE, in conferences organized in India, and included in the IEEE Xplore database. Also, there have been few qualities Honeypot surveys and/or reviews published after 2018. Furthermore, the study identified 10 classes of vital and emerging themes and/or key topics in Honeypot research. This work contributes to research efforts employing established systematic review and reporting methods in Honeypot research. We have included our meta-review methodology, in order to allow further work in this area aiming at a better understanding of the progression of Honeypot research and advances.
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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 | 0.048 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".