Utilizing Autoencoder to Generate Realistic WGAN-based Adversarial Traffic
Bibliographic record
Abstract
Intrusion Detection Systems (IDSs) play a critical role in cybersecurity by identifying and mitigating network attacks. However, adversarial attacks can exploit vulnerabilities in IDSs based on machine learning (ML) techniques, causing misclassification of malicious traffic. Traditional adversarial approaches focus on IDS evasion but neglect the functional integrity of malicious traffic, limiting practical applicability. In this paper, we propose a realism-preserving adversarial traffic generation scheme, AWGAN, which combines Wasserstein GAN (WGAN) with an autoencoder-based refinement process. The proposed scheme ensures that adversarial traffic retains its malicious characteristics while effectively evading IDS detection. In our research, we evaluate AWGAN using the CICIDS 2017 dataset and compare its performance against WGAN in two scenarios: modifying all features and modifying only non-functional features. Our experimental results demonstrate that AWGAN achieves an excellent balance between IDS evasion and traffic fidelity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".