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Detecting Fraudulent Personalities in Networks of Online Auctioneers

Detecting Fraudulent Personalities in Networks of Online Auctioneers. Duen Horng (“Polo”) Chau Shashank Pandit Christos Faloutsos. School of Computer Science Carnegie Mellon. PKDD ’06, Berlin, Germany. Duen Horng (Polo) CHAU (author of these foils – used with his permission).

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Detecting Fraudulent Personalities in Networks of Online Auctioneers

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  1. Detecting Fraudulent Personalities in Networks of Online Auctioneers Duen Horng (“Polo”) Chau Shashank Pandit Christos Faloutsos School of Computer Science Carnegie Mellon PKDD ’06, Berlin, Germany

  2. Duen Horng (Polo) CHAU (author of these foils – used with his permission) Shashank PANDIT

  3. Online auctions:very popular

  4. Why care about auction fraud? • REASON 1: it’s a serious problem • 14,500 complaints received by Internet Crime Complaint Center in USA in 2005 • Average loss per incident: > US$385 • REASON 2: it’s a hard problem • No systematic approaches, until now.

  5. Example of an online auction Very Common Non-delivery fraud We focus on dealing with it. $$$ Buyer Seller A Transaction What if something goes BAD in the transaction?

  6. Feedback on an online auction Each user has a feedback score (= # positive feedback - # negative feedback) $$$ Buyer Feedback score: 15 Seller Feedback score: 70 - 1 = 14 + 1 = 71 A Transaction

  7. How to game the feedback system?(and how to guard against gaming?)

  8. Do fraudsters follow some patterns when they boost reputation? Will never deliver Will never deliver Will never deliver Too “wasteful”; whole (near) clique will be lost

  9. They form near-bipartite cores The bad guys (humans) create 2 types of users • Accomplice • Trade mostly with honest users • Looks legitimate • Fraudster • Trade mostly with accomplices • Don’t trade with other fraudsters

  10. Why near-bipartite cores? • Allow accomplices to be reused • Hard to discover because they look very legitimate • Fraudsters will get voided, but only one at a time Will never deliver

  11. Research Goal:Detect the suspicious near-bipartite cores Our Approach: Use the belief propagation (BP) algorithm

  12. Belief Propagation (BP) algorithm Details • Efficient way to solve inference problems based on passing local messages • E.g. Used in early vision problem, such as image restoration • Useful for our problem as well! • (Thanks to John Lafferty for pointers!)

  13. Belief at each node Details Probability being honest Probability being accomplice Probability being fraudster

  14. Example Message passing is iterative. Beliefs keep being updated, until equilibrium is reached Details A E B C D

  15. Edge Compatibility Function Details • The function specifies how the belief of a node affects its neighbors (in our case, it captures the bipartite core structure) • In our context, the function can be represented as the following matrix: • Entry(i, j) = probability that a node is in state j given that it has a neighbor in state i

  16. Belief propagation -- mathematically Details Belief at a node Message to send out from a node based on its belief Edge compatibility function

  17. Experiments

  18. Confirmed Fraudsters Effectiveness on real data • Real data from eBay • 60K users; 1M edges • (more data – 12Gb/day…) Fraudsters AccomplicesHonest

  19. In the news (thanks to Byron Spice) • WSJ online • AP • LA Times • San Jose Mercury News • KDKA • USA Today

  20. Industrial etc interest • e-bay • Symantec (thanks to Bill Courtright of PDL) • ‘Belgian police’ -> probably fraudster in disguise (!?)

  21. Conclusions • Method to detect auction fraud • Use belief propagation • Detect the near bipartite cores • Evaluated with real eBay data and synthetic data

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