A comparative analysis of CGAN‐based oversampling for anomaly detection
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
Abstract In this work, the problem of anomaly detection in imbalanced datasets, framed in the context of network intrusion detection is studied. A novel anomaly detection solution that takes both data‐level and algorithm‐level approaches into account to cope with the class‐imbalance problem is proposed. This solution integrates the auto‐learning ability of Reinforcement Learning with the oversampling ability of a Conditional Generative Adversarial Network (CGAN). To further investigate the potential of a CGAN, in imbalanced classification tasks, the effect of CGAN‐based oversampling on the following classifiers is examined: Naïve Bayes, Multilayer Perceptron, Random Forest and Logistic Regression. Through the experimental results, the authors demonstrate improved performance from the proposed approach, and from CGAN‐based oversampling in general, over other oversampling techniques such as Synthetic Minority Oversampling Technique and Adaptive Synthetic.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it