How to Successfully Harness Machine Learning to Combat Fraud and Abuse

Elie Bursztein, Google

Abstract: 

While machine learning is integral to innumerable anti-abuse systems including spam and phishing detection, the road to reap its benefits is paved with numerous abuse specific challenges.

Drawing from concrete examples we will discuss how these challenges are addressed at Google and provide a roadmap to anyone interested in applying machine learning to fraud and abuse problems.

Elie Bursztein, Google

Elie Bursztein leads Google's anti-abuse research, which helps protect users against Internet threats. Elie has contributed to applied-cryptography, machine learning for security, malware understanding, and web security; authoring over fifty research papers in the field. Most recently he was involved in finding the first SHA-1 collision. Elie is a beret aficionado, blog at https://elie.net, tweets @elie, and performs magic tricks in his spare time. Born in Paris, he received a Ph.D from ENS-cachan in 2008 before working at Stanford University and ultimately joining Google in 2011. He now lives with his wife in Mountain View, California.‚Äč

BibTeX
@conference {215313,
author = {Elie Bursztein},
title = {How to Successfully Harness Machine Learning to Combat Fraud and Abuse},
year = {2018},
address = {Atlanta, GA},
publisher = {{USENIX} Association},
month = may,
}