A New CVSS-Based Tool to Mitigate the Effects of Software Vulnerabilities
Bibliographic record
Abstract
The organizations are challenged by the number of vulnerabilities in the software and hardware platforms. Successful execution of the operations need to have vulnerabilities clean environment. The U.S. National Vulnerability Database (NVD) uses Common Vulnerability Scoring System (CVSS) to score each vulnerability found and provides the detailed description of those security vulnerabilities. The score provided by the NVD is based on the intrinsic and the fundamental characteristics of a vulnerability. This score can further be refined by the organizations to calculate the bearing of the vulnerability on their environment. The purpose of CVSS is to provide a standard way to measure severity of vulnerabilities therefore CVSS version 2.0 calculator contributes less in proposing the solutions to mitigate the effects of vulnerability on a user environment. The growing number of vulnerabilities requires to have more than a simple CVSS calculator that can also propose the remediation actions for the organizations. This research paper reports on the functionality of previously developed software application to enhance the functionalities of standard CVSS version 2.0 calculator. The developed software application is capable of proposing the optimum remedial actions against vulnerabilities for organizations, requiring minimal time and efforts. This software application will be freely available for use.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".