Improving Backup System Evaluations in Information Security Risk Assessments to Combat Ransomware
Bibliographic record
Abstract
Ransomware is the fastest growing malware threat and accounts for the majority of extortion based malware threats causing billions of dollars in losses for organizations around the world. Ransomware is a global epidemic that afflicts all types of organizations that utilize computing infrastructure. Once systems are infected and storage is encrypted, victims have little choice but to pay the ransom and hope their data is released or start over and rebuild their systems. Either remedy can be costly and time consuming. However, backups can be used to restore data and systems to a known good state prior to ransomware infection. This makes backups the last line of defense and most effective remedy in combating ransomware. Accordingly, information security risk assessments should evaluate backup systems and their ability to address ransomware threats. Yet, NIST SP-800-30 does not list ransomware as a specific threat. This study reviews the ransomware process, functional backup architecture paradigms, their ability to address ransomware attacks, and provides suggestions to improve the guidance in NIST SP-800-30 and information security risk assessments to better address ransomware threats.
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 | 0.017 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".