Trend Analysis of the CVE for Software Vulnerability Management
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
Understanding vulnerability trends is a key component of the risk management process. The focus of this research is to analyze the trends of Common Vulnerabilities and Exposures (CVE) from the National Vulnerability Database (NVD) from 2007 to 2010. We extracted 22,521 CVEs through the four years, also collected their Common Vulnerability Scoring System (CVSS) scores from the NVD, then we analyzed the overall frequency, severity, and CVSS base metrics trends. Our finding shows that the frequency of all vulnerabilities decreased by 28% from 2007 to 2010; also, the percentage of high severity incidents decreased for that period. Over 80% of the total vulnerabilities were exploitable by network access without authentication. We further studied the trends of the select fifteen (15) vulnerability types which contain 18,427 vulnerabilities by analyzing their changes in frequency, severity, and CVSS base metrics. This research findings can help information security professionals focus their efforts in preventing and mitigating the impact of the attacks, and influence the development of security strategies developed by IS professionals as well.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it