Server-side Second Factors: A Statistical Approach to Measuring User Authenticity

Monday, January 25, 2016 - 4:30pm5:00pm

David Freeman, Head of Anti-Abuse Engineering, LinkedIn Corporation

Abstract: 

In this work we propose a statistical framework for measuring the validity of a login attempt. We built a prototype implementation and tested on real login data from LinkedIn using only two features: IP address and browser’s useragent. We find that we can achieve good accuracy using only user login history and reputation systems; in particular, a nascent service with no labeled account takeover data can still use our framework to protect its users. When combined with labeled data, our system can achieve even higher accuracy.

David Freeman, Head of Anti-Abuse Engineering, LinkedIn Corporation

Dr. Freeman is Head of Anti-Abuse Engineering at LinkedIn, where he leads a team of data scientists and engineers building systems to detect and prevent fraud and abuse across the LinkedIn site and ecosystem. He holds a Ph.D. in mathematics from the University of California, Berkeley, and did postdoctoral research in cryptography and security at CWI and Stanford University.

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BibTeX
@conference {206258,
author = {David Freeman},
title = {Server-side Second Factors: A Statistical Approach to Measuring User Authenticity},
year = {2016},
address = {San Francisco, CA},
publisher = {{USENIX} Association},
}