Preventing Disruption of System Backup against Ransomware Attacks
Bibliographic record
Abstract
The ransomware threat to the software ecosystem has grown rapidly in recent years. Despite being well-studied, new ransomware variants continually emerge, designed to evade existing encryption-based detection mechanisms. This paper introduces Remembrall , a new perspective to defend against ransomware by monitoring and preventing system backup disruptions. Focusing on deletion actions of volume shadow copies (VSC) in Windows, Remembrall captures related malicious events and identifies all ransomware traces as a real-time defense tool. To ensure no ransomware is missing, we conduct a comprehensive investigation to classify all potential attack actions that can be used to delete VSCs throughout the application layer, OS layer, and hardware layer. Based on the analysis, Remembrall is designed to retrieve system event information and accurately identify ransomware without false negatives. We evaluate Remembrall on recent ransomware samples. Remembrall achieves 4.31%-87.55% increase in F1-score compared to other state-of-the-art anti-ransomware tools across 60 ransomware families. Remembrall has also detected eight zero-day ransomware samples in the experiment.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".