Deep Learning-Driven Automated Data Generation for Enhanced Anomaly Detection in Cybersecurity
Bibliographic record
Abstract
In anomaly detection mechanisms for cybersecurity, researchers heavily relay on accurate and comprehensive datasets. However, researchers face persistent challenges, including incomplete data, outdated datasets, and the absence of datasets tailored to specific, contemporary scenarios. These limitations impede the development of robust anomaly detection models and exacerbate the resource-intensive nature of data preparation and analysis. Widely used datasets, such as KDD Cup, are no longer sufficient due to their age and limited applicability to modern cybersecurity environments. To address these challenges, this paper proposes a novel hybrid approach combining automation and deep learning to automate synthetic data generation. Our methodology uses contemporary testing frameworks to ensure data validity and aligns with enterprise-specific requirements. By combining automated workflows with advanced deep learning models, this framework facilitates the creation of synthetic datasets that offer enhanced domain coverage and accuracy. Furthermore, it significantly reduces computational overhead and preparation time. This research contributes to the modernization of security data generation, offering a scalable and efficient solution to enhance anomaly detection mechanisms within the domain of cybersecurity.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".