Bibliographic record
Abstract
System-call analysis is recognized as one of the most promising approaches to malware detection due to its ability to facilitate detection of malware variants as well as zero-day malware. However, one of the key challenges of system-call based analysis - which prevents it from being used in real-time detection systems - is the excessive size/dimensionality of the system-call sequences that correspond to most current day malware. The main contributions of our work are two-fold: (1) We propose a novel approach to malware system-call sequence representation that ensures more effective detection and analysis of individual malware instances as well as their corresponding malware families. In particular, our approach results in a considerable reduction in the size of system-call sequences of presented software/malware instances, while not falling victim to the so-called “dummy insertion attacks”. (2) Building upon (1), we also propose a novel supervised-learning based framework for detection of malicious system-call sequences in previously unseen software programs. This framework can also be used for effective identification and auditing of benign software programs that are not necessary malicious.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".