Reputation is the opinion of the public toward a person, a group of people, or an organization. Reputation systems are particularly important in e-markets, where they help buyers to decide whether to purchase a product or not. Since a higher reputation means more profit, some users try to deceive such systems to increase their reputation. E-markets should protect their reputation systems from attacks in order to maintain a sound environment. This work addresses the task of finding attempts to deceive reputation systems in e-markets. Our goal is to generate a list of users (sellers) ranked by the probability of fraud. Firstly we describe characteristics related to transactions that may indicate frauds evidence and they are expanded to the sellers. We describe results of a simple approach that ranks sellers by counting characteristics of fraud. Then we incorporate characteristics that cannot be used by the counting approach, and we apply logistic regression to both, improved and not improved. We use real data from a large Brazilian e-market to train and evaluate our methods and the improved set with logistic regression performs better, specially when we apply stepwise optimization. We validate our results with specialists of fraud detection in this market place. In the end, we increase by 112% the number of identified fraudsters against the reputation system. In terms of ranking, we reach 93% of average precision after specialists' review in the list that uses Logistic Regression and Stepwise optimization. We also detect 55% of fraudsters with a precision of 100%.
Electronic markets are software systems that enable online transactions between buyers and sellers. One of the major challenges in these markets is to establish the notion of trust among users. This is normally addressed by introducing a reputation system that allows users to be evaluated for each transaction they perform.This work considers the problem of detecting fraudulent behavior of users against reputation systems in Electronic Marketplaces. We select and exhibt seventeen features with good discrimination power that are effective for this task, and we conducted experiments using data from a real-world dataset from a large Brazilian marketplace, including a list of known fraudsters identified by fraud experts.As a quick and first application of these features, we find out how a minimal number of features k could be used as a stronger evidence of fraud. With k = 1 we cover as much as 97% of known frauds, but the precision is only 14.31% (F-measure 0.25). The best F-measure is 0.43 and occurs for k = 4 and k = 5. Since many sellers who fraud the reputation system are still undetected, the computed precisions are not reliable. Almost all supposed false positives with at least ten features were manually checked and confirmed by experts to have fraudulent behavior, changing precision from 47% to at least 98%, for k = 10. At the end, the fraudster list was increased by 32% by this first analysis and the largest reviewed F-measure is 0.60.
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