VINE: A Cyber Emulation Environment for MTD Experimentation

Abstract

Dynamic and moving target defenses are generally characterized by their ability to modify their own state, or the state of the protected target. As such, the evolution of these kinds of defenses require specialized experiments that can capture their behavior and effectiveness through time, as well as their broader impacts in the network. While specialized experiments can be constructed to evaluate specific defenses, there is a need for a general approach that will facilitate such tasks. In this work we introduce VINE, a high-fidelity cyber experimentation environment designed for the study and evaluation of dynamic and moving target defenses. VINE provides a common infrastructure supporting the construction, deployment, execution, and monitoring of complex mission-driven network scenarios that are fully instrumented. The tool was designed to be scalable, extensible, and highly configurable to enable the study of cyber defense strategies under dynamic background traffic and attack conditions, making VINE well-suited for the study of adaptive and moving target defenses. In this paper we introduce the VINE approach, the VINE architecture for MTD experimentation, and provide an illustrative example of the framework in action.