Normalization of Severity Rating for Automated Context-aware Vulnerability Risk Management

Normalization of Severity Rating for Automated Context-aware Vulnerability Risk Management

연구 분야: Strategies

학회: 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)

In the last three years, the unprecedented increase in discovered vulnerabilities ranked with critical and high severity raise new challenges in Vulnerability Risk Management (VRM). Indeed, identifying, analyzing and remediating this high rate of vulnerabilities is labour intensive, especially for enterprises dealing with complex computing infrastructures such as Infrastructure-as-a-Service providers. Hence there is a demand for new criteria to prioritize vulnerabilities remediation and new automated/autonomic approaches to VRM.In this paper, we address the above challenge proposing an Automated Context-aware Vulnerability Risk Management (ACVRM) methodology that aims: to reduce the labour intensive tasks of security experts; to prioritize vulnerability remediation on the basis of the organization context rather than risk severity only. The proposed solution considers multiple vulnerabilities databases to have a great coverage on known vulnerabilities and to determine the vulnerability rank. After the description of the new VRM methodology, we focus on the problem of obtaining a single vulnerability score by normalization and fusion of ranks obtained from multiple vulnerabilities databases. Our solution is a parametric normalization that accounts for organization needs/specifications.