A Recommender System for User-Specific Vulnerability Scoring
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With the inclusion of external software components in their software, vendors also need to identify and evaluate vulnerabilities in the components they use. A growing number of external components makes this process more time-consuming, as vendors need to evaluate the severity and applicability of published vulnerabilities. The CVSS score is used to rank the severity of a vulnerability, but in its simplest form, it fails to take user properties into account. The CVSS also defines an environmental metric, allowing organizations to manually define individual impact requirements. However, it is limited to explicitly defined user information and only a subset of vulnerability properties is used in the metric. In this paper we address these shortcomings by presenting a recommender system specifically targeting software vulnerabilities. The recommender considers both user history, explicit user properties, and domain based knowledge. It provides a utility metric for each vulnerability, targeting the specific organization’s requirements and needs. An initial evaluation with industry participants shows that the recommender can generate a metric closer to the users’ reference rankings, based on predictive and rank accuracy metrics, compared to using CVSS environmental score.
This work was partially supported by the Swedish Foundation for Strategic Research, grant RIT17-0035, and partially supported by the Wallenberg Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg foundation.
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