Instance-Based Learning Algorithms
Machine Learning
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Analyzing the economic efficiency of eBay-like online reputation reporting mechanisms
Proceedings of the 3rd ACM conference on Electronic Commerce
Rule induction for financial modelling and model interpretation
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Emergent properties of referral systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Avoiding ballot stuffing in eBay-like reputation systems
Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems
Trusted P2P Transactions with Fuzzy Reputation Aggregation
IEEE Internet Computing
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
Toward a Comprehensive Model in Internet Auction Fraud Detection
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Reducing internet auction fraud
Communications of the ACM - Web searching in a multilingual world
C-index: trust depth, trust breadth, and a collective trust measurement
Proceedings of the hypertext 2008 workshop on Collaboration and collective intelligence
Hit Miss Networks with Applications to Instance Selection
The Journal of Machine Learning Research
Do social networks improve e-commerce?: a study on social marketplaces
Proceedings of the first workshop on Online social networks
ACM Computing Surveys (CSUR)
Taxonomy of trust: Categorizing P2P reputation systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Management in peer-to-peer systems
A proposed data mining approach for internet auction fraud detection
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Detecting fraudulent personalities in networks of online auctioneers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
An effective early fraud detection method for online auctions
Electronic Commerce Research and Applications
Expert Systems with Applications: An International Journal
Detecting online auction shilling frauds using supervised learning
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
Reported dollar losses from online auction fraud were over $43M in 2008 in the US (NW3C, 2009). In general, reputation systems provided by online auction sites are the most common countermeasure available for buyers to evaluate a seller's credit. Unfortunately, feedback score mechanisms are too easily manipulated, creating falsely overrated reputations. In addition, existing research on online auction fraud shows that a more complicated reputation management system could weaken the motivation of committing a fraud. However, very few of the previous work addresses the most important issue of a fraud detection mechanism is to discover a fraudster before he defrauds as early as possible. Therefore, developing an effective early fraud detection mechanism is necessary to prevent fraud for online auction participants. This paper proposes a novel two-stage phased modeling framework that integrates hybrid-phased models with a successive filtering procedure to identify latent fraudsters by examining the phased features of potential fraudsters' lifecycles. This framework improves the performance of identifying latent fraudsters disguising as legitimate accounts with diverse features. In addition, a composite of measuring attributes we devised in this study is also helpful in modeling fraudulent behavior. To demonstrate the effectiveness of the proposed methods, real transaction data were collected from Yahoo!Taiwan (http://tw.bid.yahoo.com/) for training and testing. The experimental results show that the true positive rate of detecting fraudsters is over 93% on average. Furthermore, the proposed framework can significantly improve the precision and the success rate of fraud detection; the experimental results also show that the fraud detection models constructed by conventional methods are ineffective in detecting latent fraudsters.