Bibliographic record
Abstract
Due to the vast majority of obfuscation techniques employed by the malware authors, extraction of a high-level representation of malware structure is an efficient way in this regard. High-level graph representations are able to represent the main functionality of a given sample in more abstract way. The graph-based approaches have mostly revolved around static analysis of the binary and share the common drawbacks of any static based approaches. In addition to the type of analysis, the scalability of these approaches is also affected by the employed graph comparison algorithm. Full graph comparison is by itself a NP-hard problem. Approximated graph comparison algorithms such as Graph Edit Distance have been commonly studied in the field of graph classification. To address the two major weaknesses involved with the current graph-based approaches, we propose a dynamic graph-based malware classifier. At the time of this proposal, this is the first attempt to generate and classify dynamic graphs. In spite of providing more accurate graphs, dynamic analysis leads to the generating larger graphs, and aggravating the problem of comparison measurement. To address this problem we modify an existing algorithm called Simulated Annealing to reduce computational complexity. Our comparative experimental results with two other malware classifiers confirm the effectiveness of our framework.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".