LockBoost: Detecting Malware Binaries by Locking False Alarms
Bibliographic record
Abstract
High-stake applications, such as malware detection, demand a high true-positive rate (TPR) while meeting a firm (usually very low) false-positive rate (FPR, i.e., false alarms). This goal is hard to achieve using classic cost-sensitive learning, which requires a cost ratio that is difficult to specify in practice. Also, selecting a threshold point on the receiver operating characteristic (ROC) curve to meet a specified low FPR often leads to a poor TPR, as TPR and FPR adversely affect one another. To address the above requirement, we propose a novel approach called “LockBoost” that locks FPR at a specified level while iteratively boosting TPR during the training process. LockBoost produces a sequence of classifiers where each next classifier “picks up” the true positives missed due to the enforcement of the low FPR by the previous classifier. We adapt LockBoost to detect malware binaries in four datasets, including two recently released public benchmarks, SOREL and BODMAS. With FPR locked at the target level of 0.1% or lower, we achieve ≈9% and ≈7% TPR improvement over state-of-the-art methods on sequential and tabular features, 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".