Who Are You? A Statistical Approach to Measuring User Authenticity

David Mandell Freeman and Sakshi Jain, Markus Durmuth, Battista Biggio and Giorgio Giacinto

NDSS ’16


Passwords are used for user authentication by almost every Internet service today, despite a number of well known weaknesses. Numerous attempts to replace passwords have failed, in part because changing users’ behavior has proven to be difficult. One approach to strengthening password-based authentication without changing user experience is to classify login attempts into normal and suspicious activity based on a number of parameters such as source IP, geo-location, browser configuration, and time of day. For the suspicious attempts, the service can then require additional verification, e.g., by an additional phone-based authentication step. Systems working along these principles have been deployed by a number of Internet services but have never been studied publicly. In this work, we perform the first public evaluation of a classification system for user authentication. In particular:

  • We develop a statistical framework for identifying suspicious login attempts.
  • We develop a fully functional prototype implementation that can be evaluated efficiently on large datasets.
  • We validate our system on a sample of real-life login data from LinkedIn as well as simulated attacks, and demonstrate that a majority of attacks can be prevented by imposing additional verification steps on only a small fraction of users.
  • We provide a systematic study of possible attackers against such a system, including attackers targeting the classifier itself.

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