概率区域失效模型下的网络脆弱性评估

Xiaoliang Wang, Xiaohong Jiang, A. Pattavina
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引用次数: 47

摘要

关键任务网络基础设施面临着潜在的大面积威胁,既有故意的(如电磁脉冲攻击、炸弹爆炸),也有自然的(如地震、洪水)。现有的区域失效相关脆弱性研究一般采用一种简单的“确定性”区域失效模型,无法捕捉到真实区域失效场景的一些重要特征,即区域内的网络组件仅以一定的概率失效,更重要的是,该失效概率随其维数和距离失效中心的距离而变化。在本文中,我们提供了一个更一般的“概率”区域故障模型,以捕捉区域故障的关键特征,并将其应用于网络脆弱性评估。为了便于评估,我们采用基于网格划分的方案来估计随机区域故障下的各种统计网络度量。建立了一个理论框架,以确定合适的网格划分,以满足特定的估计误差要求。网格划分技术对于识别网络的脆弱区域也很有用,这可以指导网络设计人员针对此类故障启动适当的网络保护。本文的工作有助于我们更深入地理解区域故障下的网络脆弱性行为,为未来高生存能力关键任务网络的设计和维护提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing network vulnerability under probabilistic region failure model
The mission critical network infrastructures are facing potential large region threats, both intentional (like EMP attack, bomb explosion) and natural (like earthquake, flooding). The available research on region failure related vulnerability studies generally adopt a kind of simple “deterministic” region failure models, which can not capture some important features of real region failure scenarios, where a network component in the region only fails with certain probability, and more importantly, such failure probability tends to vary with both its dimension and its distance to failure center. In this paper, we provide a more general “probabilistic” region failure model to capture the key features of a region failure and apply it for the network vulnerability assessment. To facilitate such assessment, we adopt a grid partition-based scheme to estimate various statistical network metrics under a random region failure. A theoretical framework is also established to determine a suitable grid partition such that a specified estimation error requirement is satisfied. The grid partition technique is also useful for identifying the vulnerable zones of a network, which can guide network designers to initiate proper network protection against such failures. The work in this paper helps us more deeply understand the network vulnerability behavior under region failure and facilitates the design and maintenance of future highly survivable mission critical networks.
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