Vulnerability Assessment Combining CVSS Temporal Metrics and Bayesian Networks
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915760169811968 |
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| author | Perone, Stefano Guarino, Simone Faramondi, Luca Setola, Roberto |
| author_facet | Perone, Stefano Guarino, Simone Faramondi, Luca Setola, Roberto |
| contents | Vulnerability assessment is a critical challenge in cybersecurity, particularly in industrial environments. This work presents an innovative approach by incorporating the temporal dimension into vulnerability assessment, an aspect neglected in existing literature. Specifically, this paper focuses on refining vulnerability assessment and prioritization by integrating Common Vulnerability Scoring System (CVSS) Temporal Metrics with Bayesian Networks to account for exploit availability, remediation efforts, and confidence in reported vulnerabilities. Through probabilistic modeling, Bayesian networks enable a structured and adaptive evaluation of vulnerabilities, allowing for more accurate prioritization and decision-making. The proposed approach dynamically computes the Temporal Score and updates the CVSS Base Score by processing data on exploits and fixes from vulnerability databases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vulnerability Assessment Combining CVSS Temporal Metrics and Bayesian Networks Perone, Stefano Guarino, Simone Faramondi, Luca Setola, Roberto Cryptography and Security Vulnerability assessment is a critical challenge in cybersecurity, particularly in industrial environments. This work presents an innovative approach by incorporating the temporal dimension into vulnerability assessment, an aspect neglected in existing literature. Specifically, this paper focuses on refining vulnerability assessment and prioritization by integrating Common Vulnerability Scoring System (CVSS) Temporal Metrics with Bayesian Networks to account for exploit availability, remediation efforts, and confidence in reported vulnerabilities. Through probabilistic modeling, Bayesian networks enable a structured and adaptive evaluation of vulnerabilities, allowing for more accurate prioritization and decision-making. The proposed approach dynamically computes the Temporal Score and updates the CVSS Base Score by processing data on exploits and fixes from vulnerability databases. |
| title | Vulnerability Assessment Combining CVSS Temporal Metrics and Bayesian Networks |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2506.18715 |