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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".