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Server-Side Second Factors: A Statistical Approach To Measuring User Authenticity

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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.