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It is well known that computer and network security is an adversarial challenge. Attackers develop exploits and defenders respond to them through updates, service packs or other defensive measures. In non-adversarial situations, such as automobile safety, advances on one side are not countered by the other side and so progress can be demonstrated over time. In adversarial situations, advances by one side are countered by the other and so oscillatory performance typically emerges. This paper contains a detailed study of the coevolution of the Conficker Worm and associated defenses against it. It demonstrates, in concrete terms, that attackers and defenders each present moving targets to the other. After detailing specific adaptations of attackers and defenders in the context of Conficker and its variants, we briefly develop a quantitative model for explaining the coevolution based on what we call Quantitative Attack Graphs (QAG) which involve attackers selecting shortest paths through an attack graph and defenders investing in hardening the shortest path edges appropriately. Stocco, G.F. , Toward a precise simulation model for multi-agent computer security scenarios, Ph.D. thesis proposal, Dartmouth College, 2012. Abstract: Adversarial computer attacks are estimated to cost up to $1 trillion per year. Aside from the raw fiscal costs of such compromises, there is a significant lack of understanding of how vulnerable systems really are, and further how much a compromise of such systems would really cost. Models exist for the modeling of these adversarial scenarios, but require a drastic simplification of the system at study, or a priori values not based on empirical data or real world observations. These solutions do not provide meaningful information to decision makers who are faced with the highly costly dangerous adversarial environment as it exists. This thesis proposes to create a novel adaptation of signaling games, a variant of Bayesian games as a model for real world computer security scenarios. This modified signaling game allows for more realistic modeling by using empirical data of real world scenarios and a more complex extensible game framework than previous, more restrictive models. In these scenarios the Defender is the operator of a system and the Adversary may either be an external or internal threat. While most of the discussion is framed in the context of a networked server the analysis holds as well for the operation of a single system where the Adversaries control processes while the Defender controls the operating system. Our goal is to create a continuous time multi-agent game theoretic model which will verifiably simulate the above outlined real world scenario, and will allow a Defender to evaluate the effectiveness of their defense strategy based on their goals and utility function. Adversarial computer attacks are estimated to cost up to $1 trillion per year. Aside from the raw fiscal costs of such compromises, there is a significant lack of understanding of how vulnerable systems really are, and further how much a compromise of such systems would really cost. Models exist for the modeling of these adversarial scenarios, but require a drastic simplification of the system at study, or a priori values not based on empirical data or real world observations. These solutions do not provide meaningful information to decision makers who are faced with the highly costly dangerous adversarial environment as it exists. This thesis proposes to create a novel adaptation of signaling games, a variant of Bayesian games as a model for real world computer security scenarios. This modified signaling game allows for more realistic modeling by using empirical data of real world scenarios and a more complex extensible game framework than previous, more restrictive models. In these scenarios the Defender is the operator of a system and the Adversary may either be an external or internal threat. While most of the discussion is framed in the context of a networked server the analysis holds as well for the operation of a single system where the Adversaries control processes while the Defender controls the operating system. Our goal is to create a continuous time multi-agent game theoretic model which will verifiably simulate the above outlined real world scenario, and will allow a Defender to evaluate the effectiveness of their defense strategy based on their goals and utility function.