C2C Ecommerce Environment: A Pattern Based Anti-Fraud Method

C2C Ecommerce Environment: A Pattern Based Anti-Fraud Method

INTRODUCTION 

With the growing popularity of online trading sites, reputation systems are increasingly becoming an integral part of C2C ecommerce systems. Reputation systems can collect, aggregate and distribute participant feedback from past actions to encourage sellers' honest behaviors, and effectively avoid cheating behaviors of those dishonest sellers. In such a situation that neither buyers nor sellers are well informed of each other, the reputation system is able to help buyers determine which sellers are more credible. Such as eBay and Taobao[1][2][3][4], they all have their own reputation systems. The world's largest C2C online auction site eBay has a reputation system dealing with feedback information. Upon the completion of each transaction, buyers and sellers have rights to give an rating points(-1, 0, 1)of the other[5]. Code Shoppy Each participant will have an identification name, and its evaluation will be given in connection with the transaction name on it. Nowadays, many trading sites are using reputation systems like eBay's, while some of them provide 1-5 rating range or use some other rating scales. 

Some of them calculate the average feedback rating points while others calculate the cumulative ones. These reputation-rating mechanisms can’t well deal with thereputation slander, the reputation speculation and other means of fraud generally. This leads to the reputation values given by reputation systems can’t effectively reflect the performance of sellers, eventually leading to the average benefit of buyers greatly reduced. In order to deal with the fraud patterns mentioned above, Based on TRUST[8] model, we proposed a new fraud pattern identification and filtering method. It is to find fraud pattern in Time Window Scope and filter out those fraud ratings, Such as plenty of newer buyers give higher ratings over threshold or lower ratings below threshold to a fixed number of sellers, higher ratings over threshold are given by a fixed number of sellers each other, etc. In this way, the reputation value that the buyer computed will show much more fully of the true reputation of the sellers. 

The experiment results in multi-agent system JADE prove that the method proposed by us can make the sellers get more profit. The organization of the paper is as follows. The second part introduces two kinds of fraud patterns that are very regular and very hard to be recognized in ecommerce system. The third part expounds the anti-fraud method we put forward based on TRUST; part four illuminates the simulation experiment which is based on multi-agent system JADE; part five is the summary of the paper and discusses the next step of our study. 

TWO COMMON PATTERNS OF FRAUD 

A.The Reputation Slander The Reputation slander is such misconduct that some sellers encourage other sellers with partnerships or they register a number of buyers to deliberately give low-ratings to their competitors to achieve the aim of suppressing their competitors. The Reputation slander undermines the harmonious order of transaction, making high-quality sellers' reputation damaged and greatly reducing the overall transaction gains. Current C2C ecommerce systems usually take such approaches that the victims provide proof to the platform for arbitration by the manual review and recognition. Manual processing always is an inefficient and time-consuming job. 

B.The Reputation Speculation The most difficult issue in reputation system is the reputation speculation which sellers and their accomplices conduct high scores for each other or sellers register a great quantity of accounts performing virtual transactions to get high ratings. Recently, Taobao has a set of programs to monitor thereputation speculationbehavior. The first is prevention mechanism. Investigations by machines are available to filter out sellers with abnormal fluctuations apparently and they are classified as "black box". Then punishment mechanism follows. Through manual analysis, communication with the reputation speculation suspected sellers to get further confirmation. And Taobao also encourages users to report reputation speculationsellers. 

 In addition, the consumer protection play on promotion has an indirect containment for reputation speculation. The scheme is proposed to allow sellers in Taobao bet a certain amount of deposit, from which Taobao can extract partial compensation if the seller cheats consumer. However, manual processing is time-consuming, laborious and inefficient after all, so researchers are on studies of some automatic filtering of false rating mechanism.