An Empirical Study of Data Disruption by Ransomware Attacks
Bibliographic record
Abstract
The threat of ransomware to the software ecosystem has become increasingly alarming in recent years, raising a demand for large-scale and comprehensive ransomware analysis to help develop more effective countermeasures against unknown attacks. In this paper, we first collect a real-world dataset MarauderMap, consisting of 7,796 active ransomware samples, and analyze their behaviors of disrupting data in victim systems. All samples are executed in isolated testbeds to collect all perspectives of six categories of runtime behaviors, such as API calls, I/O accesses, and network traffic. The total logs volume is up to 1.98 TiB. By assessing collected behaviors, we present six critical findings throughout ransomware attacks' data reconnaissance, data tampering, and data exfiltration phases. Based on our findings, we propose three corresponding mitigation strategies to detect ransomware during each phase. Experimental results show that they can enhance the capability of state-of-the-art anti-ransomware tools. We report a preliminary result of a 41%-69% increase in detection rate with no additional false positives, showing that our insights are helpful.
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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 | 0.002 |
| 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".