Investigating Shill Bidding Behaviour Involving Colluding Bidders
Jarrod Trevathan2007, Journal of Computers
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
Shill bidding is where spurious bids are introduced into an auction to drive up the final price for the seller, thereby defrauding legitimate bidders. Trevathan and Read presented an algorithm to detect the presence of shill bidding in online auctions. The algorithm observes bidding patterns over a series of auctions, and gives each bidder a shill score to indicate the likelihood that they are engaging in shill behaviour. While the algorithm is able to accurately identify those with suspicious behaviour, it is designed for the instance where there is only one shill bidder. However, there are situations where there may be two or more shill bidders working in collusion with each other. Colluding shill bidders are able to engage in more sophisticated strategies that are harder to detect. This paper proposes a method for detecting colluding shill bidders, which is referred to as the collusion score. The collusion score, either detects a colluding group, or forces the colluders to act individually like a single shill, in which case they are detected by the shill score algorithm. The collusion score has been tested on simulated auction data and is able to successfully identify colluding shill bidders.
Related papers
A Real-Time Detection Algorithm for Identifying Shill Bidders in Multiple Online Auctions
Proceedings of the 51st Hawaii International Conference on System Sciences, 2018
Online auctions are highly susceptible to fraud. Shill bidding is where a seller introduces fake bids into an auction to drive up the final price. If the shill bidders are not detected in run-time, 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. This paper presents 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 price inflating behavior. The LSS is calculated based on the bidding patterns over a live auction and past bidding history. We have tested our algorithm 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.
A Run-Time Algorithm for Detecting Shill Bidding in Online Auctions
Journal of theoretical and applied electronic commerce research
Online auctions are a popular and convenient way to engage in ecommerce. However, the amount of auction fraud has increased with the rapid surge of users participating in online auctions. Shill bidding is the most prominent type of auction fraud where a seller submits bids to inflate the price of the item without the intention of winning. Mechanisms have been proposed to detect shill bidding once an auction has finished. However, if the shill bidder is not detected during the auction, an innocent bidder can potentially be cheated by the end of the auction. Therefore, it is essential to detect and verify shill bidding in a running auction and take necessary intervention steps accordingly. This paper proposes a run-time statistical algorithm, referred to as the Live Shill Score, for detecting shill bidding in online auctions and takes appropriate actions towards the suspected shill bidders (e.g., issue a warning message, suspend the auction, etc.). The Live Shill Score algorithm also uses a Post-Filtering Process to avoid misclassification of innocent bidders. Experimental results using both simulated and commercial auction data show that our proposed algorithm can potentially detect shill bidding attempts before an auction ends.
Real-Time Detection and Prevention of Shill Bidding in Online Auctions
2019
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...
Combating Shill Bidding in Real Time: Prevention, Detection and Response
Computer and Information Science, 2015
Human cheating has been a barrier to establishing trust among e-commerce users, throughout the last two decades. In particular, in online auctions, since all the transactions occur among anonymous users, trust is difficult to establish and maintain. Shill bidding happens when bidders bid exclusively to inflate (in forward auctions) or deflate (in reverse auctions) prices in online auctions. At present, shill bidding is the most severe and persistent form of cheating in online auctions, but still there are only a few or no established techniques for shill defense at run-time. In this paper, I evaluate the strengths and weaknesses of existing approaches to combating shill bidding. I also propose the ShillFree1 auction system to secure and protect auction systems from shill bidders for both forward and reverse auctions. More precisely, by using a variety of bidding behavior and user history, proposed auction system prevents, monitors and detects shill activities in real time. Moreover, to detect shilling thoroughly I propose IP tracking techniques. The system also takes necessary actions against shill activities at run-time. The experimental results demonstrate that, by prevention, detection and response mechanisms, the proposed auction system keeps the auction users secured from shill bidding and therefore establishes trust among online auction users.
Collusive Shill Bidding Detection in Online Auctions using Markov Random Field
Electronic Commerce Research and Applications, 2019
Shill bidding is where spurious bids are introduced into an auction to drive up the final price for the seller. This causes legitimate bidders to pay more for the item in order to win the auction. Shill bidding detection becomes more difficult when a seller involves two or more bidders and forms a collusive group to commit price-inflating behaviour. Colluding shill bidders can distribute the work evenly among each other to collectively reduce their chances of being detected. This paper presents a Collusive Shill Bidding Detection algorithm to identify the presence of colluding shill bidders. The algorithm calculates an anomaly score for each bidder and then verifies the anomaly scores to improve the detection accuracy. We use a Local Outlier Factor for calculating the anomaly score for each bidder. We then model the auction network in a Markov Random Field and apply Loopy Belief Propagation for identifying the colluding shill bidders. We implemented the proposed algorithm and applied it on both simulated and commercial auction datasets. Experimental results show that the algorithm is able to potentially detect colluding shill bidders. Comparative analysis on simulated auction datasets shows that the proposed algorithm performs better than two existing published approaches.
Shill bidding in multi-round online auctions
Proceedings of the 35th Annual Hawaii International Conference on System Sciences
The online implementation of traditional business mechanisms raises many new issues not considered in classical economic models. This partially explains why online auctions have become the most successful but also the most controversial Internet businesses in the recent years. One emerging issue is that the lack of authentication over the Internet has encouraged shill bidding, the deliberate placing of bids on the seller's behalf to artificially drive up the price of the seller's auctioned item. Private-value English auctions with shill bidding can result in a higher expected seller profit than other auction formats [1], violating the classical revenue equivalence theory. This paper analyzes shill bidding in multi-round online English auctions and proves that there is no equilibrium without shill bidding. Taking into account the seller's shills and relistings, bidders with valuations even higher than the reserve will either wait for the next round or shield their bids in the current round. Hence, it is inevitable to redesign online auctions to deal with the "shiller's curse."
A Real-Time Monitoring Framework for Online Auctions Frauds
Current Approaches in Applied Artificial Intelligence,, 2015
In spite of many advantages of online auctioning, serious frauds menace the auction users’ interests. Today, monitoring auctions for frauds is becoming very crucial. We propose here a generic framework that covers realtime monitoring of multiple live auctions. The monitoring is performed at different auction times depending on fraud types and auction duration. We divide the real-time monitoring functionality into threefold: detecting frauds, reacting to frauds, and updating bidders’ clusters. The first task examines in run-time bidding activities in ongoing auctions by applying fraud detection mechanisms. The second one determines how to react to suspicious activities by taking appropriate run-time actions against the fraudsters and infected auctions. Finally, every time an auction ends, successfully or unsuccessfully, participants’ fraud scores and their clusters are updated dynamically. Through simulated auction data, we conduct an experiment to monitor live auctions for shill bidding. The latter is considered the most severe fraud in online auctions, and the most difficult to detect. More precisely, we monitor each live auction at three time points, and for each of them, we verify the shill patterns that most likely happen.
Auctions with shill bidding
Economic Theory, 2004
Shill bidding has increased substantially in recent years since the technology employed to conduct on-line auctions enables many sellers to disguise their identities and bid. Although their intent is to gain by misleading the bidders on the value of the object, we show that in a common value auction sellers are worse off shill bidding. In fact, any out-of-auction mechanism that makes it difficult for them to shill bid increases their revenues. In addition, shill bidding reduces the surplus of the bidders and the surplus from trade. It is only the auctioneer who could gain from this activity and in that sense he may not have an incentive from within the auction to discourage shill bidding.
Scraping and Preprocessing Commercial Auction Data for Fraud Classification
Technical Paper, 2018
In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for malicious moneymakers who can commit different types of fraud activities, such as Shill Bidding (SB). The latter is predominant across many auctions but this type of fraud is difficult to detect due to its similarity to normal bidding behaviour. The unavailability of SB datasets makes the development of SB detection and classification models burdensome. Furthermore, to implement efficient SB detection models, we should produce SB data from actual auctions of commercial sites. In this study, we first scraped a large number of eBay auctions of a popular product. After preprocessing the raw auction data, we build a high quality SB dataset based on the most reliable SB strategies. The aim of our research is to share the preprocessed auction dataset as well as the SB training (unlabelled) dataset, thereby researchers can apply various machine learning techniques by using authentic data of auctions and fraud.
Related papers
Combating Shill Bidding in Online Auctions
Trust is difficult to establish in online auctions since transactions occur among complete strangers. The Internet Fraud Complaint Center shows that auction fraud is the highest rate of crime in online activities. Nowadays, shill bidding is the most severe and persistent fraud for online auction users. Considering the strengths and weaknesses of existing works on , in this paper, we propose a reliable software architecture to secure and protect auction systems from shill bidders for both forward and reverse auctions. More precisely our auction system monitors and detects shill bidding in run-time as well as takes necessary actions against shill bidding .
Online auction deception using a shill bidding agent
2008
Shill bidding is the act of using bids in an online auction to drive up the final price for the seller, thereby defrauding legitimate bidders. While 'shilling' is recognized as a problem and shill bidding is strictly forbidden in online auctions, presently there is little to no established means of defense against shills. This paper presents a software bidding agent that follows a shill bidding strategy. The agent incrementally increases an auction's price, forcing legitimate bidders to submit higher bids in order to win the item. The agent ceases bidding when the desired profit from shilling has been attained, or in the case that it is too risky to continue bidding without winning the auction. Its ability to inflate the price has been tested in a simulated marketplace and experimental results are presented. Furthermore, the agent is used to assist in developing algorithms to detect the presence of shill bidding in online auctions.
Detecting multiple seller collusive shill bidding
Electronic Commerce Research and Applications, 2021
Shill bidding occurs when fake bids are introduced into an auction on the seller's behalf in order to artificially inflate the final price. This is typically achieved by the seller having friends bid in her auctions, or the seller controls multiple fake bidder accounts that are used for the sole purpose of shill bidding. We previously
A Simple Shill Bidding Agent
4th International Conference on Information Technology: New Generations, 2007
Shill bidding is where fake bids are introduced into an auction to drive up the final price for the seller, thereby defrauding legitimate bidders. Although shill bidding is strictly forbidden in online auctions such as eBay, it is still a major problem. This paper presents a software bidding agent that follows a shill bidding strategy. The malicious bidding agent was constructed to aid in developing shill detection techniques. The agent incrementally increases an auction’s price, forcing legitimate bidders to submit higher bids in order to win the item. The agent ceases bidding when the desired profit from shilling has been attained, or in the case that it is too risky to continue bidding without winning the auction. The agent’s ability to inflate the price has been tested in a simulated marketplace and experimental results are presented. This is the first documented bidding agent that perpetrates auction fraud. We do not condone the use of the agent outside the scope of this research.
A fraud detection tool in E-auctions
2016
Due to rapid growth of the use of online auctions, fraudsters have taken advantage of these platforms to participate in their own auctions in order to raise prices (a practice called shilling). Innocent bidders have been forced to pay higher prices than they were willing to offer. This has resulted in the need to design and implement a shill detection algorithm. To eliminate this shilling problem, we designed a shilling detection algorithm integrated with an online auction. The algorithm proved to be effective and it was tested on the internet, and the short time of shill detection proved that the algorithm can work real time on e-auctions with large user base. This method can be used as a technique to eliminate shilling. Key words: E-auction, bidding, shilling, shill attributes, shill score.
Detection of Auction Fraud in Commercial Sites
Journal of Theoretical and Applied Electronic Commerce Research , 2020
Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day. Shill bidding is the predominant form of auction fraud, and it is also the most difficult to detect because it so closely resembles normal bidding behavior. Furthermore, shill bidding does not leave behind any apparent evidence, and it is relatively easy to use to cheat innocent buyers. Our goal is to develop a classification model that is capable of efficiently differentiating between legitimate bidders and shill bidders. For our study, we employ an actual training dataset, but the data are unlabeled. First, we properly label the shill bidding samples by combining a robust hierarchical clustering technique and a semi-automated labeling approach. Since shill bidding datasets are imbalanced, we assess advanced over-sampling, under-sampling and hybrid-sampling methods and compare their performances based on several classification algorithms. The optimal shill bidding classifier displays high detection and low misclassification rates of fraudulent activities.
Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning
IEEE Access, 2021
Shill Bidding (SB) occurs when the fake bidders are introduced by the seller's side to increase the final price. SB is a crime committed during the e-Auction, and it is pretty difficult to detect because of its normal bidding behavior. The bidder gets a lot of loss because he pays extra money, and the sellers benefit from shill bidding, so this article proposed a fusion base model. This proposed model is split into two parts training and validation, into 70 and 30 percent. This model has been divided into three sub-modules; the first module, two machine learning algorithms named Support vector machine (SVM), and Artificial neural network (ANN) trained parallel on the same dataset and predicting the bidding fraud. The prediction of these models becomes the input of the fuzzy-based fussed module, and fuzzy decide the actual output based on SVM and ANN predictions. On every bid, it predicts whether the fraud is committed or not. If the bidding behavior is normal, continue the bidding; otherwise, cancel the bid and block the user. The prediction accuracy of the proposed fussed machine learning approach is 99.63%. Simulation results have shown that the proposed fussed machine learning approach gives more attractive results than state-of-the-art published methods. INDEX TERMS Shill bidding, e-auction fraud, online fraud detection, deep learning model. I. INTRODUCTION Virtual Marketplace hosted on the internet is known as the E-auction. It is the process of buying and selling items through online platforms. The bidder bids the item, and the highest bidder is the winner of the item. At the beginning of the auction, bidding starts from the lowest price to a higher price depending upon the buyer's interest. The history of an auction is found in about 500 B.C when the women and the slaves were sold. In those ages, it was legal by law. In the United States, the auction was started to sell the estates, farms, and slaves, with the growth of technology, the auction was started from the computers, fax, smartphone, and many online platforms, e.g., eBay is the first online auction website started in 1995 in the United States. It is The associate editor coordinating the review of this manuscript and approving it for publication was Shenghong Li.
Understanding the impact of shill bidding in online English auctions
Increasing popularity of online auctions and the associated frauds have drawn the attention of many researchers. It is found [hal most of the auction sites prefer English auction to other auction mechanisms. The ease of adopting multiple fake idenlities over the Internet nourishes shill bidding by fraudulent sellers in English auction. In this paper we derive an equilibrium bidding strategy (0 counteract shill bidding in online English auction. Due to mere fear of cheating, the buyers may deviate from their normal behavior. Thus, there is a chance that an honest auctioneer may suffer from the loss of revenue because of lack of bidders' faith on him. Sometimes an honest bidder has to pay more due to unfair bidding practices. It is imponant !O see which auction is most suitable from bidder's and ill.lclioneer's point of view in cheating environment We also make a comparison of honest bidder's expected gain and honest auctioneer's revenue loss for three importanllypes of auctions: English auction. first price sealed-bid auction, and second price scaled-bid auction. The analysis of the results reveal that English auction should be the mOSl preferred mechanism from both honest buyer's and honesl seller's point of view. This fact can be uscd to explain the popularily of English auction over the Internet.
Bidding Agents That Perpetrate Auction Fraud
E-Business and Telecommunications Networks - Communications in Computer and Information Science 23, 2008
This paper presents a software bidding agent that inserts fake bids on the seller’s behalf to inflate an auction’s price. This behaviour is referred to as shill bidding. Shill bidding is strictly prohibited by online auctioneers, as it defrauds unsuspecting buyers by forcing them to pay more for the item. The malicious bidding agent was constructed to aid in developing shill detection techniques. We have previously documented a simple shill bidding agent that incrementally increases the auction price until it reaches the desired profit target, or it becomes too risky to continue bidding. This paper presents an adaptive shill bidding agent which when used over a series of auctions with substitutable items, can revise its strategy based on bidding behaviour in past auctions. The adaptive agent applies a novel prediction technique referred to as the Extremum Consistency (EC) algorithm, to determine the optimal price to aspire for. The EC algorithm has successfully been used in handwritten signature verification for determining the maximum and minimum values in an input stream. The agent’s ability to inflate the price has been tested in a simulated marketplace and experimental results are presented.
Online Detection of Shill Bidding Fraud Based on Machine Learning Techniques
31st International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems LNCS, SPRINGER, 2018
E-auctions have attracted serious fraud, such as Shill Bidding (SB), due to the large amount of money involved and anonymity of users. SB is difficult to detect given its similarity to normal bidding behavior. To this end, we develop an efficient SVM-based fraud classifier that enables auction companies to distinguish between legitimate and shill bidders. We introduce a robust approach to build offline the optimal SB classifier. To produce SB training data, we combine the hierarchical clustering and our own labelling strategy, and then utilize a hybrid data sampling method to solve the issue of highly imbalanced SB datasets. To avert financial loss in new auctions, the SB classifier is to be launched at the end of the bidding period and before auction finalization. Based on commercial auction data, we conduct experiments for offline and online SB detection. The classification results exhibit good detection accuracy and mis-classification rate of shill bidders.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.