What makes applying machine learning for anti-abuse fighting challenging?
COM1 Level 2
Video Conference Room, COM1-02-13
Abstract:
From protecting user inboxes from spam, to detecting phishing pages, or keeping products free of child-abuse imagery, machine learning is integral to innumerable anti-abuse systems. However, the road to extracting value from machine learning is paved with numerous abuse specific challenges.
This talk provides a roadmap to anyone interested in applying machine learning to anti-abuse problems. We start with an overview of why anti-abuse fighting is fundamentally different from other fields leveraging machine learning. We then delve into the challenges Google faced when integrating machine learning into anti-abuse systems and how we are overcame these difficulties. Finally, we conclude by discussing emerging challenges that the industry is currently facing.
Biodata:
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, 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.