Bibliographic record
Abstract
This paper presents a neuro-symbolic framework for detecting cyber compliance violations by integrating deep neural networks with symbolic rule-based reasoning. Traditional machine learning models, while effective in identifying complex patterns, often lack interpretability, limiting their use in regulated domains where explainability is essential. Conversely, symbolic systems offer transparency but are rigid and difficult to scale. Our approach unifies these paradigms by jointly optimizing predictive performance and symbolic rule consistency. Compliance knowledge is encoded as Boolean constraints and incorporated during training as a regularization objective. The model fuses neural embeddings with rule satisfaction signals to improve both accuracy and interpretability. Evaluated on real-world cybersecurity datasets, our method achieves a 99.1% accuracy, 0.96 F1-score, and a reduced false positive rate, outperforming existing baselines. The framework also provides interpretable justifications by identifying violated rules, enhancing trust and auditability. These results demonstrate the feasibility and value of neuro-symbolic systems in scalable, explainable compliance monitoring.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".