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Feature Engineering for Click Fraud Detection

FDMA 2012: International Workshop on Fraud Detection in Mobile Advertising
by

Clifton Phua

on 18 August 2013

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Transcript of Feature Engineering for Click Fraud Detection

Background Feature Engineering
for Click Fraud Detection Clifton, Eng Yeow, Ghim Eng, Kelvin, Minh
starrystarrynight Team
Institute for Infocomm Research (I2R) Problem Features Overall Temporal Spatial Potential Methods Results Conclusion Acknow-
ledgments Contact Experience Challenge Meaningful Standing on the
shoulders of giants Rewarding Fraud Publisher Clicks Competition
conditions Data How
many? What? Click behavior Click duplication High-risk
click behavior One minute
interval
duplication Six-hour
periods Countries Publisher's
top-k most
frequent
click feature Discretization Generalized Boosted
regression
Models (GBM) Bernoulli
distribution 5000
decision trees 0.001
learning rate depth of 5 5 minimum
observations starrystarrynight is
1st on test set?!? What is the underlying
click fraud scheme? What sort of concealment
strategies commonly used
by fraudulent parties? How to interpret data for
patterns of dishonest
publishers and websites? How to build effective
fraud prevention/detection
plans? SMU BuzzCity Worthy
competitors I2R Clifton Phua
cwcphua@i2r.a-star.edu.sg Questions? Our datasets and details are available at
https://sites.google.com/site/cliftonphua/
Full transcript