A Real-Time Self-Adaptive Classifier for Identifying Suspicious Bidders in Online Auctions

Abstract

With the significant increase of available item listings in popular online auction houses nowadays, it becomes nearly impossible to manually investigate the large amount of auctions and bidders for shill bidding activities, which are a major type of auction fraud in online auctions. Automated mechanisms such as data mining techniques were proven to be necessary to process this type of increasing workload. In this paper, we first present a framework of Real-Time Self-Adaptive Classifier (RT-SAC) for identifying suspicious bidders in online auctions using an incremental neural network approach. Then we introduce a clustering module that characterizes bidder behaviors in measurable attributes and uses a hierarchical clustering mechanism to create training datasets. The neural network in RT-SAC is initialized with the training datasets, which consist of labeled historical auction data.Once initialized, the network can be trained incrementally to gradually adapt to new bidding data in real-time, and thus, it supports efficient detection of suspicious bidders in online auctions. Finally, we utilize a case study to demonstrate how parameters in RT-SAC can be tuned for optimal operations and how our approach can be used to effectively identify suspicious online bidders in real-time.

A fraud detection tool in E-auctions

“…A high ABI during the final stage indicates the user's interest in the item and willingness to win the auction. ABI of a particular stage is calculated as in (Ford, 2013): Equation (1) ABI stage attribute Where Xi is the user's new bid, Yi is the user's previous bid and n is the total number of bids placed by the user in this stage.…”

“…A negative AID in the early or middle stage, combined with the number of bids (NB) placed close together, may indicate that a suspicious bidder does not want to scare off the currently active bidders and is possibly participating in bid unmasking. It is calculated as in (Ford, 2013):…”

“…A high ATUB in the early and middle stage combined with a low ATUB in the final stage is suspicious. It is calculated as the inverse of ATUB in the following formula (Ford, 2013): Equation (3) ATUB stage attribute Where Ti is the time of the user's bid and n is the total number of bids in placed by the user in that stage. If n equals 0 or 1 ATUB is set to 0 because the calculation of ATUB requires at least two bids placed by the user.…”

“…AOT is the average time that elapses when a user places a new high bid since another user placed the previous high bid (Ford, 2013). For example, if a bidder placed a bid 20 seconds after another bidder placed a bid, the outbid time would be 20 seconds.…”

“…For example, if a bidder placed a bid 20 seconds after another bidder placed a bid, the outbid time would be 20 seconds. AOT is calculated as in (Ford, 2013): Equation (4) AOT stage attribute…”