Could Min-Max Optimization be a General Defense Against Adversarial Attacks?
Bibliographic record
Abstract
Adversarial learning based on Min-Max formulations has been broadly employed in deep neural networks (DNNs) as an effective defense approach against adversarial attacks. Motivated by the level of resistance achieved by adversarial trained models against a single type of adversarial attack, in this paper we investigate if utilizing Min-Max formulation in various deep learning-based Intrusion Detection System (IDS) architectures may be considered an optimized defense against different types of state-of-the-art adversarial attacks. To investigate this, we generate adversarial samples using multiple attack methods using two benchmark IDS datasets, UNSW-NB 15 and NSL-KDD. Then, we conduct comprehensive experiments on adversarial trained models, including convolutional neural networks (CNN) and recurrent neural networks (RNN) architectures. Our results demonstrate that the adversarial IDS models that were trained against one type of attack show robustness against different adversarial attacks that could reach up to 40% higher accuracy than IDS models trained by adversarial-free (baseline) datasets. Finally, we demonstrate that training models with Carlini and Wagner (CW) adversarial samples in CNN leads to better robustness against other 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".