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Moving Target Defense (MTD) has recently emerged as a game changer in the security landscape due to its proven potential to introduce asymmetric uncertainty that gives the defender a tactical advantage over the attacker. Many different MTD techniques have been developed, but, despite the huge progress made in this area, critical gaps still exist with respect to the problem of studying and quantifying the cost and benefits of deploying MTDs. In fact, all existing techniques address a very narrow set of attack vectors, and, due to the lack of shared metrics, it is difficult to quantify and compare multiple techniques. Building on our preliminary work in this field, we propose a quantitative analytic model for assessing the resource availability and performance of MTDs, and a method for maximizing a utility function that captures the tradeoffs between security and performance. The proposed model generalizes our previous model and can be applied to a wider range of MTDs and operational scenarios to improve availability and performance by imposing limits on the maximum number of resources that can be in the process of being reconfigured. The analytic results are validated by simulation and experimentation, confirming the accuracy of our model.

Moving Target Defense (MTD) has emerged as a game changer in the security landscape, as it can create asymmetric uncertainty favoring the defender. Despite the significant work done in this area and the many different techniques that have been proposed, MTD has not yet gained widespread adoption due to several limitations. Specifically, interactions between multiple techniques have not been studied yet and a unified framework for quantifying and comparing very diverse techniques is still lacking. To overcome these limitations, we propose a framework to model how different MTD techniques can affect the information an attacker needs to exploit a system's vulnerabilities, so as to introduce uncertainty and reduce the likelihood of successful attacks. We illustrate how this framework can be used to compare two sets of MTDs, and to select an optimal set of MTDs that maximize security within a given budget. Experimental results show that our approach is effective.

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