A Recommender System for User-Specific Vulnerability Scoring

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


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.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Information Systems
Original languageEnglish
Title of host publicationCRiSIS 2019
Publication statusAccepted/In press - 2019 Aug
Publication categoryResearch
Event14th International Conference on Risk and Security of Internet and Systems, CRISIS 2019 - Hammamet, Tunisia
Duration: 2019 Oct 292019 Oct 31


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

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