{"title":"A graph-based evidence theory for assessing risk","authors":"Riccardo Santini, Chiara Foglietta, S. Panzieri","doi":"10.5281/ZENODO.32199","DOIUrl":null,"url":null,"abstract":"The increasing exploitation of the internet leads to new uncertainties, due to interdependencies and links between cyber and physical layers. As an example, the integration between telecommunication and physical processes, that happens when the power grid is managed and controlled, yields to epistemic uncertainty. Managing this uncertainty is possible using specific frameworks, usually coming from fuzzy theory such as Evidence Theory. This approach is attractive due to its flexibility in managing uncertainty by means of simple rule-based systems with data coming from heterogeneous sources. In this paper, Evidence Theory is applied in order to evaluate risk. Therefore, the authors propose a frame of discernment with a specific property among the elements based on a graph representation. This relationship leads to a smaller power set (called Reduced Power Set) that can be used as the classical power set, when the most common combination rules, such as Dempster or Smets, are applied. The paper demonstrates how the use of the Reduced Power Set yields to more efficient algorithms for combining evidences and to application of Evidence Theory for assessing risk.","PeriodicalId":297288,"journal":{"name":"2015 18th International Conference on Information Fusion (Fusion)","volume":"101 14","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 18th International Conference on Information Fusion (Fusion)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.32199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
The increasing exploitation of the internet leads to new uncertainties, due to interdependencies and links between cyber and physical layers. As an example, the integration between telecommunication and physical processes, that happens when the power grid is managed and controlled, yields to epistemic uncertainty. Managing this uncertainty is possible using specific frameworks, usually coming from fuzzy theory such as Evidence Theory. This approach is attractive due to its flexibility in managing uncertainty by means of simple rule-based systems with data coming from heterogeneous sources. In this paper, Evidence Theory is applied in order to evaluate risk. Therefore, the authors propose a frame of discernment with a specific property among the elements based on a graph representation. This relationship leads to a smaller power set (called Reduced Power Set) that can be used as the classical power set, when the most common combination rules, such as Dempster or Smets, are applied. The paper demonstrates how the use of the Reduced Power Set yields to more efficient algorithms for combining evidences and to application of Evidence Theory for assessing risk.
由于网络和物理层之间的相互依赖和联系,对互联网的日益利用导致了新的不确定性。例如,当电网被管理和控制时,电信和物理过程之间的集成就会产生认知上的不确定性。管理这种不确定性可以使用特定的框架,通常来自模糊理论,如证据理论。这种方法很有吸引力,因为它在管理不确定性方面具有灵活性,可以通过简单的基于规则的系统来管理来自异构数据源的数据。本文运用证据理论对风险进行评价。因此,作者提出了一种基于图表示的元素间具有特定属性的识别框架。当应用最常见的组合规则(如Dempster或Smets)时,这种关系导致一个更小的功率集(称为Reduced power set),它可以用作经典功率集。本文演示了如何使用约简功率集产生更有效的算法来组合证据和应用证据理论来评估风险。