Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Communications of the ACM
Computing and using reputations for internet ratings
Proceedings of the 3rd ACM conference on Electronic Commerce
Service specific anomaly detection for network intrusion detection
Proceedings of the 2002 ACM symposium on Applied computing
Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Neural Data Mining for Credit Card Fraud Detection
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
A data mining framework for constructing features and models for intrusion detection systems (computer security, network security)
Running up the bid: detecting, predicting, and preventing reserve price shilling in online auctions
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Socialtrust: tamper-resilient trust establishment in online communities
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
The SocialTrust framework for trusted social information management: Architecture and algorithms
Information Sciences: an International Journal
Informant: detecting sybils using incentives
FC'07/USEC'07 Proceedings of the 11th International Conference on Financial cryptography and 1st International conference on Usable Security
Rating the raters: a reputation system for wiki-like domains
Proceedings of the 3rd international conference on Security of information and networks
Comment classification for internet auction platforms
ADBIS'09 Proceedings of the 13th East European conference on Advances in Databases and Information Systems
ProtoTrust: an environment for improved trust management in internet auctions
ADBIS'09 Proceedings of the 13th East European conference on Advances in Databases and Information Systems
Combining ranking concept and social network analysis to detect collusive groups in online auctions
Expert Systems with Applications: An International Journal
Journal of Theoretical and Applied Electronic Commerce Research
A dynamic reputation system with built-in attack resilience to safeguard buyers in e-market
ACM SIGSOFT Software Engineering Notes
Survey: Combating online in-auction fraud: Clues, techniques and challenges
Computer Science Review
An effective early fraud detection method for online auctions
Electronic Commerce Research and Applications
Fuzzy rule optimization for online auction frauds detection based on genetic algorithm
Electronic Commerce Research
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Existing reputation systems used by online auction houses do not address the concern of a buyer shopping for commodities - finding a good bargain. These systems do not provide information on the practices adopted by sellers to ensure profitable auctions. These practices may be legitimate, like imposing a minimum starting bid on an auction, or fraudulent, like using colluding bidders to inflate the final price in a practice known as shilling.We develop a reputation system to help buyers identify sellers whose auctions seem price-inflated. Our reputation system is based upon models that characterize sellers according to statistical metrics related to price inflation. We combine the statistical models with anomaly detection techniques to identify the set of suspicious sellers. The output of our reputation system is a set of values for each seller representing the confidence with which the system can say that the auctions of the seller are price-inflated.We evaluate our reputation system on 604 high-volume sellers who posted 37,525 auctions on eBay. Our system automatically pinpoints sellers whose auctions contain potential shill bidders. When we manually analyze these sellers' auctions, we find that many winning bids are at about the items' market values, thus undercutting a buyer's ability to find a bargain and demonstrating the effectiveness of our reputation system.