Tackling uncertainty in security assessment of critical infrastructures: Dempster-Shafer Theory vs. Credal Sets Theory
Tackling uncertainty in security assessment of critical infrastructures: Dempster-Shafer Theory vs. Credal Sets Theory
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DOI：
10.1016/j.ssci.2018.04.007
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发表时间：
2018-08
期刊：
影响因子：
6.1
通讯作者：
A. Misuri;N. Khakzad;G. Reniers;V. Cozzani
中科院分区：
文献类型：
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作者：
A. Misuri;N. Khakzad;G. Reniers;V. Cozzani
确保关键基础设施的安全是一项复杂的任务。所需信息通常很少或不存在，专家的判断可能不准确且有偏见。本文提出了两种处理数据稀缺性、不精确性和不确定性的方法：证据网络和信用网络。证据网络是一种基于Dempster-Shafer理论的图形技术，用于显式地建模变量之间的认知不确定性的传播，而Credal网络是贝叶斯网络的扩展，用于处理基于专家判断的概率集，称为Credal集。这两种方法构成了强有力的框架，可以解释数据的高度不精确性，尽管信息量很低，但仍能产生信息量很大的结果。在目前的研究中，这两种方法在表达不确定性的权力已经显示，并通过他们的应用程序的安全漏洞评估的案例研究，他们的差异进行了描述。结果表明，两种方法在预测分析中具有实质等价性，因此，提出了一种通过等价Credal网络对证据网络进行近似更新的方法，以克服Dempster-Shafer理论中无法计算更新的问题。
Securing critical infrastructures is a complex task. Required information is usually scarce or inexistent, and experts’ judgments may be inaccurate and biased. In this paper, two methodologies dealing with data scarcity, imprecision, and uncertainty are presented: Evidential network and Credal network. Evidential network is a graphical technique based on Dempster-Shafer Theory to explicitly model the propagation of epistemic uncertainty among variables while Credal network is an extension of Bayesian network to deal with sets of probabilities, known as Credal sets, based on experts’ judgments. Both methodologies constitute robust frameworks to account for high degree of imprecision on data, producing informative results despite the low-informative input. In the present study, the power in expressing uncertainty of these two methodologies have been showed, and their differences have been described through their application to a case study of security vulnerability assessment. Results demonstrate the substantial equivalence of the two methodologies in prognostic analysis, thus, an approximate updating procedure of Evidential network through equivalent Credal network has been proposed, to overcome the lack of possibility to compute updating in the context of Dempster-Shafer Theory.