Detecting Fraudulent Personalities in Networks of Online Auctioneers Duen Horng (“Polo”) Chau...

Detecting Fraudulent Personalities in Networks of Online Auctioneers

Duen Horng (“Polo”) ChauShashank Pandit

Christos Faloutsos

School of Computer ScienceCarnegie Mellon

PKDD ’06, Berlin, Germany

“Detecting Fraudulent Personalities in Networks of Online Auctioneers” Polo Chau, Shashank Pandit, Christos Faloutsos

School of Computer Science, Carnegie Mellon University, USA2

Duen Horng (Polo) CHAU(author of these foils –used with his permission)

Shashank PANDIT

Online auctions:very popular



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.



$$$

Example of an online auction

Seller Buyer

A TransactionWhat if something goes BAD in the transaction?

Non-delivery fraud

Very Common

We focus on dealing with it.



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)

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



Do fraudsters follow some patterns when they boost reputation?

Too “wasteful”; whole (near) clique will be lost

Will never deliverWill never deliver

Will never deliver



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



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

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!)

Details



Belief at each node

Probability being fraudster

Probability being accomplice

Probability being honest

Details



Example Message passing is iterative.Beliefs keep being updated, until equilibrium is reached

A

C

B

E

D

Details



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



Belief propagation -- mathematically Details

Message to send out from a node based on its belief

Belief at a node

Edge compatibility function

Experiments



FraudstersAccomplices Honest

ConfirmedFraudsters

Effectiveness on real data Real data from eBay

60K users; 1M edges (more data – 12Gb/day…)



In the news

(thanks to Byron Spice) WSJ online AP LA Times San Jose Mercury News KDKA USA Today



Industrial etc interest

e-bay Symantec (thanks to Bill Courtright of PDL) ‘Belgian police’ -> probably fraudster in

disguise (!?)



Conclusions

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 Duen Horng (“Polo”) Chau...

Documents

Transcript of Detecting Fraudulent Personalities in Networks of Online Auctioneers Duen Horng (“Polo”) Chau...