A Recommender System for User-Specific Vulnerability Scoring
Karlsson, Linus LU
; Nikbakht Bideh, Pegah
LU
and Hell, Martin
LU
(2020)
14th International Conference on Risk and Security of Internet and Systems, CRISIS 2019
In Lecture Notes in Computer Science
12026.
p.355-364
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/83dd4b29-3471-4c90-9bb2-ba14990ec364
- author
- Karlsson, Linus
LU
; Nikbakht Bideh, Pegah
LU
and Hell, Martin
LU
- organization
- publishing date
- 2020
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- CRiSIS 2019: Risks and Security of Internet and Systems
- series title
- Lecture Notes in Computer Science
- volume
- 12026
- pages
- 355 - 364
- publisher
- Springer
- conference name
- 14th International Conference on Risk and Security of Internet and Systems, CRISIS 2019
- conference location
- Hammamet, Tunisia
- conference dates
- 2019-10-29 - 2019-10-31
- external identifiers
-
- scopus:85082102633
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 978-3-030-41568-6
- DOI
- 10.1007/978-3-030-41568-6
- project
- Säkra mjukvaruuppdateringar för den smarta staden
- language
- English
- LU publication?
- yes
- id
- 83dd4b29-3471-4c90-9bb2-ba14990ec364
- date added to LUP
- 2019-08-26 16:13:09
- date last changed
- 2024-03-19 18:45:33
@inproceedings{83dd4b29-3471-4c90-9bb2-ba14990ec364,
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.}},
author = {{Karlsson, Linus and Nikbakht Bideh, Pegah and Hell, Martin}},
booktitle = {{CRiSIS 2019: Risks and Security of Internet and Systems}},
isbn = {{978-3-030-41568-6}},
issn = {{0302-9743}},
language = {{eng}},
pages = {{355--364}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science}},
title = {{A Recommender System for User-Specific Vulnerability Scoring}},
url = {{https://lup.lub.lu.se/search/files/68795201/recommender_cameraready.pdf}},
doi = {{10.1007/978-3-030-41568-6}},
volume = {{12026}},
year = {{2020}},
}