Advertisement

A Recommender System for User-Specific Vulnerability Scoring

  • Linus KarlssonEmail author
  • Pegah Nikbakht Bideh
  • Martin Hell
Conference paper
  • 49 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12026)

Abstract

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.

Notes

Acknowledgements

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.

References

  1. 1.
    Aggarwal, C.C.: Recommender Systems. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-29659-3CrossRefGoogle Scholar
  2. 2.
    Chen, L., Sycara, K.: WebMate: a personal agent for browsing and searching. In: Proceedings of the Second International Conference on Autonomous Agents, AGENTS 1998, pp. 132–139. ACM (1998)Google Scholar
  3. 3.
    Farris, K.A., Shah, A., Cybenko, G., Ganesan, R., Jajodia, S.: Vulcon: a system for vulnerability prioritization, mitigation, and management. ACM Trans. Priv. Secur. (TOPS) 21(4), 1–28 (2018)CrossRefGoogle Scholar
  4. 4.
    First: Common vulnerability scoring system v3.0: Specification document. https://www.first.org/cvss/specification-document
  5. 5.
    Gadepally, V.N., et al.: Recommender systems for the department of defense and the intelligence community. MIT Lincoln Laboratory (2016)Google Scholar
  6. 6.
    Lee, Y., Shin, S.: Toward semantic assessment of vulnerability severity: a text mining approach. In: 1st International Workshop on EntitY REtrieval (EYRE 2018) (2018)Google Scholar
  7. 7.
    Liu, Q., Zhang, Y.: VRSS: a new system for rating and scoring vulnerabilities. Comput. Commun. 34, 264–273 (2011)CrossRefGoogle Scholar
  8. 8.
    Mell, P.M., et al.: A complete guide to the common vulnerability scoring system version 2.0 (2007). https://www.nist.gov/publications/complete-guide-common-vulnerability-scoring-system-version-20
  9. 9.
    Van Meteren, R., Van Someren, M.: Using content-based filtering for recommendation. In: Proceedings of ECML 2000 Workshop: Machine Learning in Information Age, pp. 47–56 (2000)Google Scholar
  10. 10.
    MITRE Corporation: CVE details. https://www.cvedetails.com/
  11. 11.
    NIST: National vulnerability database. https://nvd.nist.gov/
  12. 12.
    Rapid7: Vulnerability and exploit database. https://www.rapid7.com/db
  13. 13.
    Smyth, B.: Case-based recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-72079-9_11CrossRefGoogle Scholar
  14. 14.
    Spanos, G., Sioziou, A., Angelis, L.: WIVSS: a new methodology for scoring information systems vulnerabilities. In: Proceedings of the 17th Panhellenic Conference on Informatics, PCI 2013, pp. 83–90. ACM, New York (2013)Google Scholar
  15. 15.
    Yao, Y.Y.: Measuring retrieval effectiveness based on user preference of documents. J. Am. Soc. Inf. Sci. 46(2), 133–145 (1995)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Linus Karlsson
    • 1
    Email author
  • Pegah Nikbakht Bideh
    • 1
  • Martin Hell
    • 1
  1. 1.Department of Electrical and Information TechnologyLund UniversityLundSweden

Personalised recommendations