Advanced

A Recommender System for User-specific Vulnerability Scoring (full version)

Karlsson, Linus LU ; Nikbakht Bideh, Pegah LU and Hell, Martin LU (2019)
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... (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:
author
organization
publishing date
type
Other contribution
publication status
published
subject
pages
17 pages
language
English
LU publication?
yes
id
20b8d9bb-7e38-4634-b889-047a3292b282
date added to LUP
2019-08-26 16:42:57
date last changed
2019-08-27 14:47:47
@misc{20b8d9bb-7e38-4634-b889-047a3292b282,
  abstract     = {With the inclusion of external software components in their software, vendors also need to identify and evaluate <br/>vulnerabilities in the components they use.<br/>A growing number of external components makes this process more time-consuming, as vendors need to evaluate the severity and applicability of published vulnerabilities.<br/>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.<br/>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.<br/>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},
  language     = {eng},
  pages        = {17},
  title        = {A Recommender System for User-specific Vulnerability Scoring (full version)},
  year         = {2019},
}