Fraud detection in reputation systems in e-markets using logistic regression

Authors:
Rafael Maranzato;Adriano Pereira;Alair Pereira do Lago;Marden Neubert
Affiliations:
Universo Online Inc., São Paulo, SP, Brazil;Federal Univ. of Minas Gerais, Belo Horizonte, MG, Brazil;Univ. of São Paulo - USP, São Paulo, SP, Brazil;Universo Online Inc., São Paulo, SP, Brazil
Venue:
Proceedings of the 2010 ACM Symposium on Applied Computing
Year:
2010

Citing 9
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Abstract

Reputation systems are specially important in e-markets, where they help buyers to decide whether or not to purchase a product. 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. First 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 performes better. The list with 32.1% of topmost probable fraudsters against the reputation system was selected. We increased by 110% the number of identified fraudsters against the reputation system and no false positives were confirmed.