Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning
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 m...
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doaj-2383bc021f6b4095a855104316b678512021-08-23T23:01:24ZengIEEEIEEE Access2169-35362021-01-01911361211362110.1109/ACCESS.2021.30986289491143Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine LearningWajhe Ul Husnian Abidi0https://orcid.org/0000-0001-5165-2947Mohammad Sh. Daoud1https://orcid.org/0000-0003-2682-9231Baha Ihnaini2https://orcid.org/0000-0002-2109-4793Muhammad Adnan Khan3https://orcid.org/0000-0003-4854-9935Tahir Alyas4https://orcid.org/0000-0003-0938-3127Areej Fatima5https://orcid.org/0000-0002-7264-7941Munir Ahmad6https://orcid.org/0000-0002-5240-0984Department of Computer Science, Lahore Garrison University, Lahore, PakistanCollege of Engineering, Al Ain University, Al Ain, United Arab EmiratesDepartment of Computer Science, Wenzhou-Kean University, Wenzhou, ChinaDepartment of Software, Pattern Recognition and Machine Learning Laboratory, Gachon University, Seongnam, South KoreaDepartment of Computer Science, Lahore Garrison University, Lahore, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore, PakistanSchool of Computer Science, National College of Business Administration and Economics, Lahore, PakistanShill 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.https://ieeexplore.ieee.org/document/9491143/Shill biddinge-auction fraudonline fraud detectiondeep learning model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wajhe Ul Husnian Abidi Mohammad Sh. Daoud Baha Ihnaini Muhammad Adnan Khan Tahir Alyas Areej Fatima Munir Ahmad |
spellingShingle |
Wajhe Ul Husnian Abidi Mohammad Sh. Daoud Baha Ihnaini Muhammad Adnan Khan Tahir Alyas Areej Fatima Munir Ahmad Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning IEEE Access Shill bidding e-auction fraud online fraud detection deep learning model |
author_facet |
Wajhe Ul Husnian Abidi Mohammad Sh. Daoud Baha Ihnaini Muhammad Adnan Khan Tahir Alyas Areej Fatima Munir Ahmad |
author_sort |
Wajhe Ul Husnian Abidi |
title |
Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning |
title_short |
Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning |
title_full |
Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning |
title_fullStr |
Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning |
title_full_unstemmed |
Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning |
title_sort |
real-time shill bidding fraud detection empowered with fussed machine learning |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Shill bidding e-auction fraud online fraud detection deep learning model |
url |
https://ieeexplore.ieee.org/document/9491143/ |
work_keys_str_mv |
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1721198017964408832 |