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Attack graph-based security metrics: Concept, taxonomy, challenges and open issues

Authors :
Al-Araji Zaid. J.
Sakinah Syed Ahmad Sharifah
Farhood Hussein M.
Awad Mutlag Ammar
Al-Khaldee Mahmood S.
Source :
BIO Web of Conferences, Vol 97, p 00085 (2024)
Publication Year :
2024
Publisher :
EDP Sciences, 2024.

Abstract

Context: Security issues have increased recently because of the increased use of networking. The researchers have proposed many models, approaches, and models, for example, attack graphs. The attack graph model is a valuable tool for vulnerability analysis as well as for displaying all network paths. In general, attack graphs can be utilized for a variety of purposes, including the calculation of security metrics. Nonetheless, in order to sufficiently safeguard networks, a technique for gauging the security degree provided by these activities is required, as “you cannot improve what you cannot measure.” The security level of a system or network is typically represented by network security metrics in qualitative and quantitative ways. The network security metrics are typically employed to evaluate a system's security level and meet security objectives. Aim: This study aims to present a review of attack graph-based security metrics and analyse the previous work. Provides the limitations and issues the researchers faced to improve this important research area. Methodology: The attack graph security metrics field was thoroughly investigated in all research, and four databases—ScienceDirect, Web of Science (WoS), Scopus, and IEEE—were used to collect data between 2001 and 2022. Results: 46 papers were founded on attack graph security metrics with different methods and techniques based on the exclusion and inclusion criteria. The results of the taxonomy created three significant categories: proposed, implemented, reviewed, and surveyed. We believe this study will aid in highlighting research ability, which will subsequently broaden and establish new research topics.

Details

Language :
English, French
ISSN :
21174458
Volume :
97
Database :
Directory of Open Access Journals
Journal :
BIO Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.8041e45d4a4a6fb30ee92273313af9
Document Type :
article
Full Text :
https://doi.org/10.1051/bioconf/20249700085