Chain-of-Thought Augmented Fine‑Tuning of a Distilled Llama‑8B Model for SIEM Detection Query Generation
Bibliographic record
Abstract
Writing effective security detection rules for SIEM systems is a complex and timeconsuming task that traditionally requires deep domain expertise.One of the persistent challenges in applying AI to security operations (SecOps) is the scarcity of high-quality, domain-specific datasets that can support the development of accurate and reliable models.This paper addresses that gap by presenting a method to both generate such a dataset and leverage it to fine-tune a compact reasoning-oriented model (DeepSeek R1 Distill Llama 8B) using Low Rank Adaptation (LoRA).As part of our contributions, we detail the creation of a curated, high-quality (of 1206 entries from 106 detection rule) dataset specifically tailored for SIEM text-to-query tasks, which enabled effective fine-tuning of the model.A key feature of this dataset is the augmentation of each training example with chain-ofthought (CoT) rationales: step-by-step explanations linking the natural language description of a detection rule to the resulting Lucene query.These rationales, produced by a stronger teacher model (Claude sonnet 4), are used to supervise the smaller student model.We describe the data pipeline, prompt templates, and LoRA configuration, and we report an initial human evaluation showing that CoT augmentation improves the reliability of text→query generation without increasing computational cost.Despite its compact size, our fine-tuned model outperformed several large proprietary language models in both query accuracy and reasoning quality.DeepSeek R1 Distill Llama 8B was chosen as a small model with reasoning capabilities, while Claude Sonnet 4 was selected for its strong ability to generate rationales.However, the research remains applicable to other small reasoning models and large, more capable models, respectively.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".