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Erschienen in:
2008 | OriginalPaper | Buchkapitel
An Attack Graph-Based Probabilistic Security Metric
verfasst von : Lingyu Wang, Tania Islam, Tao Long, Anoop Singhal, Sushil Jajodia
Erschienen in: Data and Applications Security XXII
Verlag: Springer Berlin Heidelberg
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To protect critical resources in today’s networked environments, it is desirable to quantify the likelihood of potential multi-step attacks that combine multiple vulnerabilities. This now becomes feasible due to a model of causal relationships between vulnerabilities, namely, attack graph. This paper proposes an attack graph-based probabilistic metric for network security and studies its efficient computation. We first define the basic metric and provide an intuitive and meaningful interpretation to the metric. We then study the definition in more complex attack graphs with cycles and extend the definition accordingly. We show that computing the metric directly from its definition is not efficient in many cases and propose heuristics to improve the efficiency of such computation.