An empirical study of ransomware attacks on organizations: an assessment of severity and salient factors affecting vulnerability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study looks at the experiences of organizations that have fallen victim to ransomware attacks. Using quantitative and qualitative data of 55 ransomware cases drawn from 50 organizations in the UK and North America, we assessed the severity of the crypto-ransomware attacks experienced and looked at various factors to test if they had an influence on the degree of severity. An organization’s size was found to have no effect on the degree of severity of the attack, but the sector was found to be relevant, with private sector organizations feeling the pain much more severely than those in the public sector. Moreover, an organization’s security posture influences the degree of severity of a ransomware attack. We did not find that the attack target (i.e. human or machine) or the crypto-ransomware propagation class had any significant bearing on the severity of the outcome, but attacks that were purposefully directed at specific victims wreaked more damage than opportunistic ones.
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 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 it