Fraud Detection in Reputation Systems in e-Markets using Logistic Regression

摘要

Reputation systems axe 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.