Abstract
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.
Highlights
Virtual Marketplace hosted on the internet is known as the E-auction
Yellow is is define the Shill Bidding (SB) detection while the dark blue area is defined that the normal bid, while the area between the yellow and blue is may or may not, SB depend upon the rule which we describe in the membership functions
Tab. 3 describes there are actual 1689 bids that are normal in which Artificial neural network (ANN) 1686 truly predicts while three wrong predictions
Summary
Virtual Marketplace hosted on the internet is known as the E-auction It is the process of buying and selling items through online platforms. In SB, bidding item prices are increased by fake bids As these are real-time bids, so it’s difficult to detect because of their normal resemblance behaviour. In 2010 another person was caught committing the SB fraud, and he paid off the fine of £50,000 Another man used two accounts, the first account is selling an item, and by using the second account, he is fake bidding to increase the cost. This man was fined £5,000 under the newly introduced law[3]. There are several SB frauds are accused, so according to the past data and the behaviour of SB victims, we can be creating the cloud base model which can be integrated into the online platforms and overview the different accounts activity, and according to their actions, the proposed model will be able to shortlist the suspicious accounts that can be helpful to overcome the SB
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