Machine Learning and Large Language Models-based Techniques for Cyber Threat Detection: A Comparative Study
Bibliographic record
Abstract
This study presents a comparative analysis of Machine Learning (ML) and Large Language Models (LLMs) for Cyber Threat Detection. We evaluate the performance of various ML algorithms (e.g. Random Forest, Gradient Boosting) and fine-tuned LLM algorithms (e.g. LlaMA3, Falcon) on multiple datasets, considering metrics such as F1-score, real-world applicability, explainability, interpretability, scalability, and adaptability to evolving threats. Our results show that while ML models often have strong performance and interpretability, LLMs show the potential for high accuracy, especially when dealing with complex hazard patterns. However, the computational requirements and ambiguities associated with LLMs present challenges to widespread adoption. To maximize the benefits of both approaches, we propose several future research directions leveraging both techniques. Future research should focus on improving the interpretability of LLM, reducing the computational cost, and building a synergistic solution harnessing ML models and LLMs.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".