Improving Backup System Evaluations in Information Security Risk Assessments to Combat Ransomware
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.031 |
| Open science | 0.001 | 0.001 |
| 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