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.
Original languageEnglish
Title of host publicationCRiSIS 2019: Risks and Security of Internet and Systems
Pages 355-364
ISBN (Electronic)978-3-030-41568-6
Publication statusPublished - 2020
Event14th International Conference on Risk and Security of Internet and Systems, CRISIS 2019 - Hammamet, Tunisia
Duration: 2019 Oct 292019 Oct 31

Publication series

Name Lecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Conference on Risk and Security of Internet and Systems, CRISIS 2019

Subject classification (UKÄ)

  • Information Systems


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