NeuroYara: Learning to Rank for Yara Rules Generation Through Deep Language Modeling and Discriminative N-Gram Encoding
Why this work is in the frame
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Bibliographic record
Abstract
Signature-based malware detection methods are recognized for their simplicity, explainability, and efficiency. One of the most commonly used tools is Yara, which provides the syntax for crafting malware signatures. However, while developing high-quality Yara rules requires significant expertise in malware analysis, training such skilled analysts can be both resource-intensive and time-consuming. While a few works have been conducted to automate the generation of signatures, signatures generated by those works typically underperform the manually generated ones. In addition, these automated methods often depend on large static databases of hard-coded byte n-grams to minimize false positives. Instead of storing a large non-inclusive database to score byte n-grams, we propose a novel architecture utilizing two learning to rank neural networks to understand the underlying effectiveness and correlations among n-grams extracted for rule construction. This approach provides better flexibility and coverage of possible n-grams while reducing the required storage size from several GBs to only 10MBs. Combining these two models with a hierarchical density-based clustering method allows us to group multiple n-grams into logical conditions as Yara rules of higher quality. Experimental results show that our framework, NeuroYara, reduces the resources invested by analysts while generating rules with a low false-positive rate outperforming existing tools and manually-generated rules.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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