###### Abstract  
In the context of vulnerability management for data communication networks, determining which vulnerabilities to address first is of paramount importance. While identifying vulnerabilities using network scanners is relatively straightforward, efficiently prioritizing them for mitigation remains a significant challenge. Previously, our team developed a machine learning-based converter to translate CVSS v2.0 base scores into CVSS v3.x base scores, specifically to enable the use of the CVSS v3.x environmental score. The central question of this research is whether leveraging these converter-enabled CVSS v3.x environmental scores leads to a measurably improved vulnerability prioritization process compared to traditional methods, often reliant solely on CVSS v2.0 base scores. The environmental score potentially offers a more refined, context-specific perspective on vulnerability impact within specific systems. To evaluate this approach, we will test the converter's performance in real-world environments and assess its impact on network administrator decision-making and workflows. Performance improvement will be measured by analyzing changes in mitigation times, potential threat exposure reduction, and overall vulnerability management efficiency. The ultimate goal is to determine if the proposed machine learning based methodology delivers practical benefits, enhancing organizational security through more accurate and effective vulnerability prioritization.Experimental results demonstrate that CVSS v3.x environmental scoring resolves critical v2.0 imprecision issues, enabling more accurate vulnerability prioritization. Our approach achieves measurable efficiency gains, reducing estimated remediation work hours by up to 8% compared to CVSS v2.0 methods. The study confirms that the proposed methodology delivers practical benefits, enhancing organizational security through more accurate and effective vulnerability prioritization. c 2025 15 8787 2025 1  
As stated in [4], only a few of the newly reported vulnerabilities would be actively used for exploitation. However, the number of accumulated vulnerabilities has passed 200,000 and many of them have matured exploits that still present a real threat. Consequently, cyber security experts face the challenging task of identifying critical vulnerabilities and prioritizing remediation efforts effectively [2; 3; 4]. To assess vulnerability criticality in an environmental context, one needs to calculate the environment score. In the case of environmental calculations using the Common Vulnerability Scoring System v2.0, variables such as the potential of collateral damage (\(CDP\) ), target distribution (\(TD\)), confidentiality (\(C\)), integrity (\(I\)) and availability (\(A\)) must be taken into account in the overall score. The CVSS v2.0 environmental score contains significant limitations [5], while the absence of CVSS v3.x base scores for legacy vulnerabilities prevent using superior v3.x environmental scoring. Therefore, in this contribution, we utilize a novel approach, adopting a machine learning conversion mechanism, which allows us to calculate the CVSS v3.x base score knowing the CVSS v2.0 base score [6; 7]. Once all base CVSS v3.x scores are known, the CVSS v3.x environmental scores can be calculated [8]. Finally, vulnerability prioritization can be fully implemented using the CVSS v3.x standard. Compared with commercially available vulnerability scanners, the proposed approach offers a transparent way in which the prioritization process is applied, allowing the end user to understand the rationale behind the automatic decisions made by the presented Vulnerability Management Center (VMC) in version 1.0.2 software [9]. According to the authors' knowledge, such an approach has not been presented before in the publicly available literature. Thus, in summary, the major contribution of this paper is a practical implementation of the vulnerability management process using CVSS 3.x environmental score. This paper is divided into the following subsections.