Using Machine Learning to Analyze and Detect Anomalies in SELinux Security Policies
Bibliographic record
Abstract
Analysis of Security-Enhanced Linux (SELinux) policies requires extensive manual effort to identify violations and security mis-configurations. Current tools employ mathematical abstractions that, while theoretically sound, produce outputs that practitioners struggle to interpret effectively. Automated approaches using machine learning have shown promise but fail to capture the complex relationships inherent in SELinux policies. Here we present a novel approach combining graph-based policy representation with neural networks to automate SELinux policy analysis. Our approach represents policies as graph structures and transforms these structures into meaningful vector embeddings to learn continuous feature representations that preserve policy neighborhoods and violation patterns. We develop a flexible policy analysis framework that processes these representations through Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) models to detect violations. Our experimental results demonstrate that this approach achieves 95% accuracy in identifying security violations while maintaining balanced precision and recall metrics, significantly outperforming existing analysis techniques. Through extensive evaluation on synthetic policy datasets derived from production systems, we show that our method effectively captures diverse violation patterns including separation of duty violations, domain transition issues, and unauthorized access paths. Overall, our work presents an efficient approach for automated, interpretable SELinux policy analysis that bridges the gap between theoretical security models and practical policy management.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".