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Security metrics serve as a powerful tool for organizations to understand theeffectiveness of protecting computer networks. However majority of thesemeasurement techniques don't adequately help corporations to make informed riskmanagement decisions. In this paper we present a stochastic security frameworkfor obtaining quantitative measures of security by taking into account thedynamic attributes associated with vulnerabilities that can change over time.Our model is novel as existing research in attack graph analysis do notconsider the temporal aspects associated with the vulnerabilities, such as theavailability of exploits and patches which can affect the overall networksecurity based on how the vulnerabilities are interconnected and leveraged tocompromise the system. In order to have a more realistic representation of howthe security state of the network would vary over time, a nonhomogeneous modelis developed which incorporates a time dependent covariate, namely thevulnerability age. The daily transition-probability matrices are estimatedusing Frei's Vulnerability Lifecycle model. We also leverage the trusted CVSSmetric domain to analyze how the total exploitability and impact measuresevolve over a time period for a given network.

A Predictive Framework for Cyber Security Analytics using Attack Graphs