Vulnerability Metrics for Graph-based Configuration Security

Iganibo, Ibifubara; Albanese, Massimiliano; Mosko, Marc; Bier, Eric A.; Brito, Alejandro E.

doi:10.5220/0010559402590270

Cited by 1 publication

(5 citation statements)

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“…A "good" attack surface metric should be able to identify and credibly enumerate all attack paths 6 by conducting an in-depth analysis of each path's entry and exit points or target points, implicit and explicit interdependencies, and vulnerabilities. 7 Stuckman and Purtilo highlighted that an attack surface metric quantifies the scale of vulnerabilities of a system. 6 They suggested that, in analyzing a system's security posture, a good metric will demonstrate that software with fewer vulnerabilities is more secure than software with more exposures.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, we focus our attention on features of the vulnerabilities themselves and on any information that may be available to potential attackers and that could influence their selection of target exploits. 7,19 We define the exploitation likelihood (or simply likelihood) of a vulnerability as the probability that an attacker will attempt to exploit that vulnerability, if given the opportunity. An attacker has the opportunity to exploit a vulnerability if certain preconditions are met, most notably if they have access to the vulnerable host and have sufficient privileges.…”

Section: Exploit Likelihoodmentioning

confidence: 99%

“…The susceptibility of a system to attacks is determined by the area of its attack surface. A “good” attack surface metric should be able to identify and credibly enumerate all attack paths 6 by conducting an in‐depth analysis of each path's entry and exit points or target points, implicit and explicit interdependencies, and vulnerabilities 7 …”

Section: Related Workmentioning

confidence: 99%

“…In this subsection, we review the metrics 7 we have developed to address the need to quantify different dimensions of the vulnerability subgraphs used in SCIBORG's model. For the purpose of this analysis, we consider a static snapshot of the system being studied, so that we can assume that the set of known vulnerabilities is fixed.…”

Section: Technical Backgroundmentioning

confidence: 99%

“…While approaches considering the skills and resources available to different types of attackers have been explored in the literature, 14 such approaches are not useful in practice, as defenders should always operate under worst‐case assumptions, and assume they are facing skilled and well‐equipped attackers. Therefore, we focus our attention on features of the vulnerabilities themselves and on any information that may be available to potential attackers and that could influence their selection of target exploits 7,19 …”

Section: Technical Backgroundmentioning

confidence: 99%

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An attack volume metric

Iganibo

Albanese

Mosko

et al. 2023

Security and Privacy

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For more than a decade, the notion of attack surface has been used to define the set of vulnerable assets that an adversary may exploit to penetrate a system, and various metrics have been developed to quantify the extent of a system's attack surface. However, most approaches to tackle this problem have failed to consider the complex interdependencies that exist between the many components of a distributed system, its vulnerabilities, and its configuration parameters. In our work, building upon previous research on vulnerability metrics and on graphical models to capture such interdependencies, we propose a novel approach to evaluate the potential risk associated with exposed vulnerabilities by studying how the effect of each vulnerability exploit propagates through chains of dependencies. Our analysis goes beyond the scope of traditional attack surface metrics, and considers the depth and implications of potential attacks, leading to the definition of a new family of metrics, which we refer to as attack volume metrics. We present experimental results illustrating how the proposed metric scales for graphs of realistic sizes, and illustrate its application to real‐world testbeds.

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Section: Related Workmentioning

confidence: 99%

Section: Exploit Likelihoodmentioning

confidence: 99%