To Label or to Pseudo Label? Active Learning vsSemi-Supervised Learning for Windows Malware Prediction
Bibliographic record
Abstract
Malware poses a pervasive threat to system security and global cybersecurity, demanding continuous vigilance and innovation in detection methods. Traditional signature-based approaches are laborious and susceptible to concept drift. In this paper, we focus on the malware detection task and explore Active Learning and Semi-Supervised Learning methodologies to enhance detection accuracy, particularly in scenarios with limited labeled data. Using a Microsoft malware prediction dataset we use Random Forest and XGBoost as base models and assess the impact of different techniques including uncertainty sampling, query by committee, self-training, and co-training. Our goal is to understand which approach presents more advantages, given that in this deployment scenario obtaining a large volume of labeled samples is very expensive and time-consuming. We observe that both active learning and semi-supervised techniques exhibit performance gains, but the base learner employed has a crucial importance. Moreover, semi-supervised learning has better outcomes in certain settings, even with the disadvantage of not using any human provided labels.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".