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. 1. Introduction Online auction portals have become a convenient trading platform for a great number of sellers and buyers. Besides eBay [1], the Aukro auction portal [2], a Czech Republic division of the multinational Allegro group with a turnover of USD 250 million and 2.5 million users as of January 2012, is an example of such an Internet auction system. Online auctions allow their users to buy or sell products and services. A large number of users use Internet auctions as their main source of income. For example, the Aukro states [2] that, from 2.5 million users, 9,300 users are professional traders. On the other hand, the number of frauds in online auctions is also increasing [3]. The most common types of fraud are: intentionally incorrect description of goods, undelivered goods, irredeemable payments, sale of stolen goods, and others [4]. Fraudsters are attracted by low admission costs and high profit potential. In order to reduce frauds, Internet auction portals use basically three methods:(1)built-in mechanisms which allow estimation of the credibility of users: auction systems create user profiles (reputation) that are based on the evaluation which is performed by users after performed transaction. Any user can look at the profile of another user before she/he will do business with her/him; if this user (a bidder or a seller) has a bad reputation, no one will want
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