An Ensemble Learning to Detect Decision-Based Adversarial Attacks in Industrial Control Systems
Why this work is in the frame
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Bibliographic record
Abstract
An increasing number of Intrusion Detection Systems (IDSs) rely on Artificial Intelligence (AI), specifically Ma-chine Learning (ML) algorithms, to distinguish between benign and malicious data and detect cyber attacks. However, using ML algorithms exposes IDSs to Adversarial Machine Learning (AML) attacks during the training and test phase. These AML attacks aim to deceive ML algorithms by misclassifying data, posing significant disruptions to the system and its users. Two critical categories of AML attacks are White-box and Black-box attacks, with Black-box attacks being more practical and representative of real-world scenarios. This paper investigates the impact of adversarial examples on supervised ML models in IDSs and proposes an ensemble learning-based detection approach. The study uses a power system dataset and employs Random Forest, AdaBoost, and Decision Tree classifiers to achieve this. During the test phase, adversarial examples are generated using the decision boundary and HopSkipJump attacks, two types of Black-box decision-based attacks. The research applies a deep neural network to the dataset containing the generated adversarial examples to detect these AML attacks, achieving an accuracy of 98 % to 99 %.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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