A Bayesian Probit Online Model Framework for Auction Fraud Detection
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2013, Vol 3, Issue 4
Abstract
Individual users are able to buy and sell a broad variety of goods and services worldwide on online auction and shopping websites, e.g. eBay.com and Taobao.com. However, attackers have also attempted to conduct fraudulent activities against honest parties for the purpose of illegitimate profit. On Internet auction sites, auction fraud mainly involves fraud attributable to the non-delivery of products purchased through an Internet auction site or the misrepresentation of a product advertised for sale. Malicious sellers may post a non existing item for bidding with false description to deceive the buyer concerning its true value, and request payments to be wired directly to them. Similarly, malicious buyers may make a purchase through a fraudulent credit card where the address of the card holder does not match the shipping address. Both consumers as well as merchants can be victims of online auction fraud, as well as the commercial auction websites. In this paper we study the problem of building models for the online auction fraud detection system, which essentially evolves dynamically over time. We propose a Bayesian probit online model framework for auction fraud detection.
Authors and Affiliations
Sirisha Potluri
Keywords
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