Analyzing the Quality of Synthetic Adversarial Cyberattacks
Bibliographic record
Abstract
Today's networked systems face significant security challenges due to sophisticated attacks. Several Machine Learning (ML) and Deep Learning (DL) models are employed to combat these diverse attacks. Adversarial attacks, which can evade detection by AI-based intrusion detection systems (IDS) through small alterations to network attack traffic, pose a significant concern. These AI-synthesized adversarial attacks must adhere to network constraints to seem plausible. In this work, we explore the validation criteria for such adversarial attacks and propose a methodology for analyzing their quality. We evaluate adversarial attack samples synthesized by state-of-the-art generative DL models such as Variational autoencoder (VAE), Conditional Variational autoencoder (CVAE), Generative Adversarial Network (GAN) and compare the performance with our CVAE-Adversarial Network (CVAE-AN) model. Results indicate the effectiveness of CVAE-AN in synthesizing realistic adversarial attacks.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".