title: Real-Time Detection and Prevention of Shill Bidding in Online Auctions
Creators: name: Majadi, Nazia
Publisher: name: Griffith University
Year: 2019
Subjects: subject: Shill bidding
subject: Online auctions
subject: Real-time detection
subject: Live Shill Score (LSS)
subject: Fake bidding

description: Online auctions have become one of the most popular and convenient buying and selling media in e-commerce. However, the amount of auction fraud increases with the popu- larity of online auctions. This thesis examines one of the most severe types of auction fraud, referred to as shill bidding, where fake bids are used to arti cially in ate an item's nal price. Shill bidding is strictly prohibited in online auctions because it forces honest bidders to pay more for their products. Researchers have proposed several mechanisms to detect shill bidding once an auction has nished. However, if shill bidding is not detected during an auction, an innocent bidder (i.e., the winner of the auction) can potentially be cheated by the end of the auction. Therefore, it is necessary to detect and verify potential shill bidding in real-time (i.e., while an auction is in progress). This thesis proposes and implements several novel techniques for combating shill bidding in real-time. The e ectiveness of these techniques is shown through applica- tions on simulated and commercial auction datasets. The research results include the following: 1. Existing literature conducted on shill bidding detection and prevention has been studied. The research gaps that remain as research challenges in this particular domain have been identi ed. The research ideas on the detection and prevention of shill bidding have been summarised from past literature. This will help future researchers to develop more trustworthy online auction environments. 2. Testing the e ectiveness of newly proposed fraud countermeasures is a very chal- lenging task. The reason is the lack of auction data availability as commercial online auction houses (e.g., eBay, Yahoo! Auctions, etc.) do not share their data for privacy concerns. Furthermore, there is no way to evaluate the performance of fraud detection mechanisms. To accomplish these research goals, an online auc- tion system, referred to as uAuction, has been developed. uAuction has been used to test the e ectiveness of newly proposed shill bidding detection algorithms. 3. A real-time shill bidding detection algorithm, referred to as the Live Shill Score (LSS), has been introduced to identify shill bidding while an auction is in progress. Experimental analysis shows that the LSS algorithm can detect potential shill bidders at the 80% duration mark of an online auction in most cases, which is 20% quicker at detecting potential shill bidding than the existing published proposals. 4. As real-world auction data contains a large number of auctions and participants, the LSS algorithm has been extended to multiple auctions. The extended ver- sion of real-time shill bidding detection algorithm has been referred to as the multi-auction LSS. The algorithm has been implemented with a single seller. Experimentation has been undertaken to determine how e ective the algorithm is in identifying shill bidders across multiple auctions. However, some innocent bidders were incorrectly classi ed as shill bidders when the algorithm considered multiple auctions hosted by only a single seller. To avoid this misclassi cation, the multi-auction LSS algorithm has been considered for multiple auctions con- ducted by multiple sellers. Experimental results show that the algorithm is able to accurately detect potential shill bidding across multiple auctions. Comparative analysis shows that the multi-auction LSS with multiple sellers performs better than the multi-auction LSS with a single seller and the LSS algorithm. 5. Detecting potential shill bidders becomes more di cult when a seller involves two or more bidders and forms a collusive group to commit shill bidding in his/her own auction(s). The reason is colluding shill bidders can distribute the work evenly among each other to collectively reduce their chances of being detected. An o ine approach to detect colluding shill bidders in online auctions based on machine learning techniques has been proposed. The solution is referred to as the Collusive Shill Bidding Detection (CSBD). The CSBD algorithm has been implemented and applied on both simulated and commercial auction datasets. Experimental results show that the CSBD algorithm can detect potential colluding shill bidders. Later, the algorithm has been extended to a real-time approach, referred to as the real- time CSBD algorithm. The real-time CSBD has been implemented and applied on simulated and commercial auction datasets. Experimental analysis shows that the algorithm is able to highlight potential colluding shill bidders in real-time. 6. Shill bidding detection becomes more complicated when a seller registers multiple fake accounts and engages shill bidders in the auctions operated by those fake seller accounts. Such behaviour is referred to as multiple seller collusive shill bidding, where a seller can distribute the risk between the colluding sellers and reduce their chance of identifying any particular seller (i.e., a shill bidder). There is no lit- erature available on the detection of multiple seller collusive shill bidding in real-time. A basic real-time detection algorithm using social network analy- sis (SNA) has been proposed for identifying multiple seller collusive shill bidding while an auction is in progress. The algorithm has been referred to as the Multi- ple Seller Collusive Shill Bidding Detection (MSCSBD). The algorithm has been implemented and applied on synthetically generated auction dataset. Experimen- tal results show that the algorithm behaves reasonably and can identify potential colluding sellers who engage in shill bidding while an auction is in progress.