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)
Shashank PANDIT
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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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.
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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We focus on dealing with it.
$$$
Seller Buyer
A Transaction
What if something goes BAD in the transaction?
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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Each user has a feedback score (= # positive feedback - # negative feedback)
$$$
Seller
Feedback score: 70 + 1 = 71
A Transaction
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
Buyer
Feedback score: 15 - 1 = 14
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How to game the feedback system?
(and how to guard against gaming?)

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
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
10

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
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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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
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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Research Goal:
Detect the suspicious near-bipartite cores
Our Approach:
Use the belief propagation (BP) algorithm

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!)
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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Probability being honest
Probability being accomplice
Probability being fraudster
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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Example
Message passing is iterative.
Beliefs keep being updated, until equilibrium is reached
A E
B
C
D
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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

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
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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Belief propagation -- mathematically
Belief at a node
Message to send out from a node based on its belief
Edge compatibility function
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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Experiments

Effectiveness on real data
 Real data from eBay


60K users; 1M edges
(more data – 12Gb/day…)
Confirmed
Fraudsters
Fraudsters
Accomplices
Honest
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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(thanks to Byron Spice)
 WSJ online
 AP
 LA Times
 San Jose Mercury News
 KDKA
 USA Today
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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 e-bay
 Symantec (thanks to Bill Courtright of PDL)

‘Belgian police’ -> probably fraudster in disguise (!?)
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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 Method to detect auction fraud


Use belief propagation
Detect the near bipartite cores
 Evaluated with real eBay data and synthetic data
“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos
School of Computer Science, Carnegie Mellon University, USA
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