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Uncertain Reasoning for Detection of Selling Stolen Goods in Online Auctions Using Contextual Information

机译:使用上下文信息检测在线拍卖中被盗商品的不确定原因

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This work describes the design of a decision support system for detection of fraudulent behavior of selling stolen goods in online auctions. In this system, each seller is associated with a type of certification, namely “proper seller,” “suspect seller,” and “selling stolen goods.” The certification level is determined on the basis of a seller’s behaviors and especially on the basis of contextual information whose origin is outside online auctions portals. In this paper, we focus on representing knowledge about sellers in online auctions, the influence of additional information available from other Internet source, and reasoning on bidders’ trustworthiness under uncertainties using Dempster-Shafer theory of evidence. To demonstrate the practicability of our approach, we performed a case study using real auction data from Czech auction portal Aukro. The analysis results show that our approach can be used to detect selling stolen goods. By applying Dempster-Shafer theory to combine multiple sources of evidence for the detection of this fraudulent behavior, the proposed approach can reduce the number of false positive results in comparison to approaches using a single source of evidence.
机译:这项工作描述了决策支持系统的设计,该系统用于检测在线拍卖中销售被盗商品的欺诈行为。在此系统中,每个卖方都与一种证书类型相关联,即“适当的卖方”,“可疑卖方”和“出售被盗货物”。认证级别取决于卖方的行为,尤其是基于上下文信息,该上下文信息的来源来自在线拍卖门户网站之外。在本文中,我们着重于使用Dempster-Shafer证据理论来表示关于在线拍卖中卖方的知识,可从其他Internet来源获得的其他信息的影响,以及不确定性下对投标人可信度的推理。为了证明我们方法的可行性,我们使用来自捷克拍卖门户网站Aukro的真实拍卖数据进行了案例研究。分析结果表明,我们的方法可用于检测被盗货物的销售情况。通过应用Dempster-Shafer理论来组合多种证据来源来检测这种欺诈行为,与使用单一证据来源的方法相比,所提出的方法可以减少假阳性结果的数量。

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