Investigating Resistance of Deep Learning-based IDS against Adversaries\n using min-max Optimization
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Bibliographic record
Abstract
With the growth of adversarial attacks against machine learning models,\nseveral concerns have emerged about potential vulnerabilities in designing deep\nneural network-based intrusion detection systems (IDS). In this paper, we study\nthe resilience of deep learning-based intrusion detection systems against\nadversarial attacks. We apply the min-max (or saddle-point) approach to train\nintrusion detection systems against adversarial attack samples in NSW-NB 15\ndataset. We have the max approach for generating adversarial samples that\nachieves maximum loss and attack deep neural networks. On the other side, we\nutilize the existing min approach [2] [9] as a defense strategy to optimize\nintrusion detection systems that minimize the loss of the incorporated\nadversarial samples during the adversarial training. We study and measure the\neffectiveness of the adversarial attack methods as well as the resistance of\nthe adversarially trained models against such attacks. We find that the\nadversarial attack methods that were designed in binary domains can be used in\ncontinuous domains and exhibit different misclassification levels. We finally\nshow that principal component analysis (PCA) based feature reduction can boost\nthe robustness in intrusion detection system (IDS) using a deep neural network\n(DNN).\n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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