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Feature models to boost the vulnerability management process?

delete2023-01-01
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Á
Ángel Jesús Varela‐Vaca *
D
Diana Borrego
M
María Teresa Gómez-López
R
Rafael M. Gasca
A
A. German Márquez
DOI:10.1016/j.jss.2022.111541delete
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摘要

摘要

En 中文
Vulnerability management is a critical and very challenging process that allows organisations to design a procedure to identify potential vulnerabilities, assess the level of risk, and define remediation mechanisms to address threats. Thus, the large number of configuration options in systems makes it extremely difficult to identify which configurations are affected by vulnerabilities and even assess how systems may be affected. There are several repositories to store information on systems, software vul-nerabilities, and exploits. However, they are largely scattered, offer different formats and information, and their use has limitations, complicating vulnerability management automation. For this reason, we introduce a discussion concerning modelling in vulnerability management and the proposal of feature models as a means to collect the variability of software and system configurations to facilitate the vulnerability management process. This paper presents AMADEUS-Exploit, a feature model -based solution that provides query and reasoning mechanisms that make it easier for vulnerability management experts. The power of AMADEUS-Exploit is shown and evaluated in three different ways: first, the solution is compared with other vulnerability management tools; second, the solution is faced with another in a complex scenario with 4,000 vulnerabilities and 700 exploits; and finally, our solution was used in a real project demonstrating the usability of reasoning operations to determine potential vulnerabilities. (c) 2022 Elsevier Inc. All rights reserved.
Keyword:
Cybersecurity
Feature model
Vulnerability
Exploits
Reasoning
Vulnerable management process
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期刊

Journal of Systems and Software 封面图
Journal of Systems and Software
IF:
4.1
论文数:
5.5K
被引数:
8.4K

机构

U
University of Sevilla
学者数:
1.9W
论文数: 1.7W
被引数: 15
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