Generative-Adversarial Class-Imbalance Learning for Classifying Cyber-Attacks and Faults - A Cyber-Physical Power System
Bibliographic record
Abstract
There has been an increasing interest in the use of data-driven techniques for classifying cyber-attacks and physical faults in cyber-physical systems. In real-world applications, the number of cyber-attack and faulty samples is usually far less than normal samples. This causes the skewed class distribution in data collected from cyber-physical systems. Training an accurate predictive model under skewed class conditions is not an easy task. In this work, we introduce a new generative adversarial framework for learning from skewed class distributions. This novel Adversarial Class-Imbalance Learning (ACIL) scheme has a novel loss function that is used during the adversarial training session. ACIL tries to iteratively adjust weights of an auxiliary multilayer perceptron to learn the minority class (i.e., cyber-attacks and physical faults) distributions along with the majority class (i.e., normal) distribution. Moreover, we devise an inclusive data-driven scheme for classifying cyber-attacks and faults, which includes four experiments of a baseline, nine state-of-the-art class-imbalance learning methods, two different generative-adversarial network-based approaches, and ACIL. These techniques are verified and compared through several experimental cyber-physical power scenarios. The obtained results show the effectiveness of ACIL for classifying samples of cyber-attacks and faults with skewed class distributions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".