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CURATOR
A pinboard by
Nazia Majadi

PhD Student, Griffith University

PINBOARD SUMMARY

My research topic is to detect shill bidding fraud in running auctions & take actions to fraudsters.

An online auction provides ease, comfort and convenient trading environment to its users. Therefore, online auction houses (e.g., eBay) are extremely popular for sellers and buyers. However, despite the overwhelming benefits of online auctions, they are highly susceptible to fraud. Online auction fraud is one of the fastest growing forms of Internet-based crime. The U.S. Federal Bureau of Investigation’s Internet Crime Complaint Center reveals that auction related complaints are still ranking at the top of the complaints list with approximately 116,292 complaints in 2015 (https://pdf.ic3.gov/2015_IC3Report.pdf). As participants are anonymous, both sellers and bidders can be involved in fraudulent activities. One of the most common types of online auction fraud is shill bidding. Shill bidding is the act of introducing false bids into an auction on behalf of the seller to artificially raise the item’s price so that legitimate bidders must pay more in order to win. The presence of shill bidding also diminishes the auction houses’ reputation as bidders will be reluctant to participate if they feel there is the possibility of being ripped-off by a seller. Shill bidding is difficult to detect because: (i) any user can register under false identity; and (ii) multiple users can form a collusive bidding group to evade detection. Furthermore, it is not easy to prove that someone is indeed guilty of shill bidding. Most of the existing literatures focus on examining bidding patterns once an auction has terminated. However, if the shill bidders are not detected in running auctions, innocent bidders will have already been cheated by the time the auction ends. Therefore, it is necessary to detect shill bidders in real-time and take appropriate actions according to the fraud activities. My research area is to propose a real-time shill bidding detection algorithm to identify the presence of shill bidding in multiple online auctions. The algorithm provides each bidder a Live Shill Score (LSS) indicating the likelihood of their potential involvement in shill bidding. The LSS is calculated based on the bidding patterns over live auctions and past bidding history. The proposed algorithm has been tested on data obtained from a series of realistic simulated auctions and also commercial online auctions. Experimental results show that the real-time detection algorithm is able to prune the search space required to detect which bidders are likely to be potential shill bidders.

4 ITEMS PINNED

A dynamic stage-based fraud monitoring framework of multiple live auctions

Abstract: Abstract Monitoring the progress of auctions for fraudulent bidding activities is crucial for detecting and stopping fraud during runtime to prevent fraudsters from succeeding. To this end, we introduce a stage-based framework to monitor multiple live auctions for In-Auction Fraud (IAF). Creating a stage fraud monitoring system is different than what has been previously proposed in the very limited studies on runtime IAF detection. More precisely, we launch the IAF monitoring operation at several time points in each running auction depending on its duration. At each auction time point, our framework first detects IAF by evaluating each bidder’s stage activities based on the most reliable set of IAF patterns, and then takes appropriate actions to react to dishonest bidders. We develop the proposed framework with a dynamic agent architecture where multiple monitoring agents can be created and deleted with respect to the status of their corresponding auctions (initialized, completed or cancelled). The adoption of dynamic software architecture represents an excellent solution to the scalability and time efficiency issues of IAF monitoring systems since hundreds of live auctions are held simultaneously in commercial auction houses. Every time an auction is completed or terminated, the participants’ fraud scores are updated dynamically. Our approach enables us to observe each bidder in each live auction and manage his fraud score as well. We validate the IAF monitoring service through commercial auction data. We conduct three experiments to detect and react to shill-bidding fraud by employing datasets acquired from auctions of two valuable items, Palm PDA and XBOX. We observe each auction at three-time points, verifying the shill patterns that most likely happen in the corresponding stage for each one.AbstractMonitoring the progress of auctions for fraudulent bidding activities is crucial for detecting and stopping fraud during runtime to prevent fraudsters from succeeding. To this end, we introduce a stage-based framework to monitor multiple live auctions for In-Auction Fraud (IAF). Creating a stage fraud monitoring system is different than what has been previously proposed in the very limited studies on runtime IAF detection. More precisely, we launch the IAF monitoring operation at several time points in each running auction depending on its duration. At each auction time point, our framework first detects IAF by evaluating each bidder’s stage activities based on the most reliable set of IAF patterns, and then takes appropriate actions to react to dishonest bidders. We develop the proposed framework with a dynamic agent architecture where multiple monitoring agents can be created and deleted with respect to the status of their corresponding auctions (initialized, completed or cancelled). The adoption of dynamic software architecture represents an excellent solution to the scalability and time efficiency issues of IAF monitoring systems since hundreds of live auctions are held simultaneously in commercial auction houses. Every time an auction is completed or terminated, the participants’ fraud scores are updated dynamically. Our approach enables us to observe each bidder in each live auction and manage his fraud score as well. We validate the IAF monitoring service through commercial auction data. We conduct three experiments to detect and react to shill-bidding fraud by employing datasets acquired from auctions of two valuable items, Palm PDA and XBOX. We observe each auction at three-time points, verifying the shill patterns that most likely happen in the corresponding stage for each one.

Pub.: 01 Jan '17, Pinned: 18 Sep '17