Fuzzy rule optimization for online auction frauds detection based on genetic algorithm

Abstract

Due to the huge amount of users and low entrance cost of online auction, there are a lot of online fraud cases in online auction sites. According to the IC3 reports from 2003 to 2010, we can understand the fraud cases and victims are increasing rapidly year by year. To improve the prevention of online auction frauds, this research will propose a hybrid approach to detect the fraudster accounts to help the users to identify which seller is more dangerous. In this research, we use social network analysis to produce the behavior features and transform these features into fuzzy rules which can represent the detection rules. Then optimize the fuzzy rules by genetic algorithms to build the auction fraud detection model. For implementation, we collect the real auction data from the online auction site http://www.ruten.com.tw which is the most popular auction site in Taiwan. Finally, we use the proposed features and methodologies to detect the fraudster accounts and find out the detection models of them. We hope the result of this research can help the website administrators to detect the possible collusive fraud groups easier in online auction.