Tracing the Ransomware Bloodline: Investigation and Detection of Drifting Virlock Variants
Bibliographic record
Abstract
Malware, especially ransomware, has dramatically increased in volume and sophistication in recent years. The growing complexity and destructive potential of ransomware demand effective countermeasures. Despite tremendous efforts by the security community to document these threats, reliance on manual analysis makes it challenging to discern unique malware variants from polymorphic variants. Moreover, the easy accessibility of source code of prominent ransomware families in public domains has led to the rise of numerous variants, complicating manual detection and hindering the identification of phylogenetic relationships. This paper introduces a novel approach that narrows the focus to analyze one such prominent ransomware family, Virlock. Using binary code similarity, we systematically reconstruct the lineage of Virlock, tracing its relationships, evolution, and variants. Employing this technique on a dataset of over 1000 Virlock samples submitted to VirusTotal and VirusShare, our analysis unveils intricate relationships within the Virlock ransomware family, offering valuable insights into the tangled relationships of this ransomware.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".