VINE:用于MTD实验的网络仿真环境

T. Eskridge, Marco M. Carvalho, Evan Stoner, Troy Toggweiler, A. Granados
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引用次数: 22

摘要

动态和移动目标防御的特点通常是它们能够修改自己的状态或被保护目标的状态。因此,这类防御的演变需要专门的实验,可以随着时间的推移捕捉它们的行为和有效性,以及它们在网络中的广泛影响。虽然可以构建专门的实验来评估特定的防御,但需要一种通用的方法来促进此类任务。在这项工作中,我们介绍了VINE,这是一个高保真的网络实验环境,旨在研究和评估动态和移动目标防御。VINE提供了一个通用的基础设施,支持构建、部署、执行和监控完全仪器化的复杂任务驱动网络场景。该工具具有可扩展性、可扩展性和高度可配置性,能够研究动态背景流量和攻击条件下的网络防御策略,使VINE非常适合研究自适应和移动目标防御。在本文中,我们介绍了VINE方法,用于MTD实验的VINE体系结构,并提供了一个实际的框架示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VINE: A Cyber Emulation Environment for MTD Experimentation
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.
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