Fraudulent auctions on the Internet
Electronic Commerce Research
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
The Role of Reputation Systems in Reducing On-Line Auction Fraud
International Journal of Electronic Commerce
A typology of complaints about eBay sellers
Communications of the ACM - The psychology of security: why do good users make bad decisions?
Reducing internet auction fraud
Communications of the ACM - Web searching in a multilingual world
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Fraud detection in reputation systems in e-markets using logistic regression
Proceedings of the 2010 ACM Symposium on Applied Computing
Using Stereotypes to Identify Risky Transactions in Internet Auctions
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
A novel two-stage phased modeling framework for early fraud detection in online auctions
Expert Systems with Applications: An International Journal
A machine-learned proactive moderation system for auction fraud detection
Proceedings of the 20th ACM international conference on Information and knowledge management
Internet Auction Fraud Detection Using Social Network Analysis and Classification Tree Approaches
International Journal of Electronic Commerce
Combining ranking concept and social network analysis to detect collusive groups in online auctions
Expert Systems with Applications: An International Journal
Online modeling of proactive moderation system for auction fraud detection
Proceedings of the 21st international conference on World Wide Web
A Supervised Learning Process to Elicit Fraud Cases in Online Auction Sites
SYNASC '11 Proceedings of the 2011 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
SYNASC '12 Proceedings of the 2012 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Detecting online auction shilling frauds using supervised learning
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Non-delivery fraud is a recurring problem at online auction sites: false sellers that list nonexistent products just to receive payments and afterwards disappear, possibly repeating the swindle with another identity. In our work we identified a set of publicly available features related to listings, sellers and product categories, and built a machine learning system for fraud prediction taking into account the high class imbalance of real data and the need to control the false positives rate due to commercial reasons. We tested the proposed system with data collected from a major Brazilian online auction site, obtaining good results on the identification of fraudsters before they strike, even when they had no previous historical information. We also evaluated the contribution of category-related features to fraud detection. Finally, we compared the learning algorithm used (boosted trees) with other state-of-the-art methods.