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. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 2 Duen Horng (Polo) CHAU (author of these foils – used with his permission) Shashank PANDIT

3. Online auctions: very popular

4. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 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. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 5 Potential Buyer CPotential Buyer B Potential Buyer A $$$ Example of an online auction Seller $ $$ Buyer A Transaction

6. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 6 $$$ Example of an online auction Seller Buyer A Transaction What if something goes BAD in the transaction? Non-delivery fraud Very Common We focus on dealing with it.

7. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 7 Buyer Feedback score: 15 $$$ Feedback on an online auction Seller Feedback score: 70 A Transaction + 1 = 71 - 1 = 14 Each user has a feedback score (= # positive feedback - # negative feedback)

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

9. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 9

10. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 10 Do fraudsters follow some patterns when they boost reputation? Too “wasteful”; whole (near) clique will be lost Will never deliver

11. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 11 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

12. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 12 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

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

14. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 14 Belief Propagation (BP) algorithm 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!) Details

15. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 15 Belief at each node Probability being fraudster Probability being accomplice Probability being honest Details

16. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 16 Example Message passing is iterative. Beliefs keep being updated, until equilibrium is reached A C B E D Details

17. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 17 Edge Compatibility Function 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 Details

18. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 18 Belief propagation -- mathematically Details Message to send out from a node based on its belief Belief at a node Edge compatibility function

19. Experiments

20. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 20 Fraudsters Accomplices Honest Confirmed Fraudsters Effectiveness on real data Real data from eBay 60K users; 1M edges (more data – 12Gb/day…)

21. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 21

22. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 22 In the news (thanks to Byron Spice) WSJ online AP LA Times San Jose Mercury News KDKA USA Today

23. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 23 Industrial etc interest e-bay Symantec (thanks to Bill Courtright of PDL) ‘Belgian police’ -> probably fraudster in disguise (!?)

24. “Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos School of Computer Science, Carnegie Mellon University, USA 24 Conclusions Method to detect auction fraud Use belief propagation Detect the near bipartite cores Evaluated with real eBay data and synthetic data