Increasing Detection Rate for Imbalanced Malicious Traffic using Generative Adversarial Networks
Bibliographic record
Abstract
Intrusion Detection and Prevention Systems aim to detect and prevent malicious activity or policy violations. Anomaly-based models like autoencoders or neural networks have become prominent because they do not rely on pre-defined signatures. In this study, we leverage the generative abilities of a Wasserstein Generative Adversarial Network + Gradient Penalty (WGAN-GP) to create anomalies to combat class imbalance artificially. We compare its performance on the CSE-CIC-IDS2018 data set from the Canadian Institute for Cybersecurity Intrusion Detection System (CIC-IDS) with two other anomaly-based models and one discriminative model. Our model Data-Imbalanced Aware XGBoost (DIAX) excels with an F1 score of 96.90% that shows great performance to combat class imbalances. Additionally, we conduct a detailed analysis using SHapley Additive exPlanations (SHAP) to interpret predictions of the best-performing model. Lastly, we argue that SHAP can help in the task of dimensionality reduction for classifiers.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".