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Risk-based authentication (RBA) aims to protect users against attacksinvolving stolen passwords. RBA monitors features during login, and requestsre-authentication when feature values widely differ from previously observedones. It is recommended by various national security organizations, and usersperceive it more usable and equally secure than equivalent two-factorauthentication. Despite that, RBA is still only used by very few onlineservices. Reasons for this include a lack of validated open resources on RBAproperties, implementation, and configuration. This effectively hinders the RBAresearch, development, and adoption progress.  To close this gap, we provide the first long-term RBA analysis on areal-world large-scale online service. We collected feature data of 3.3 millionusers and 31.3 million login attempts over more than one year. Based on thedata, we provide (i) studies on RBA's real-world characteristics, and itsconfigurations and enhancements to balance usability, security, and privacy,(ii) a machine learning based RBA parameter optimization method to supportadministrators finding an optimal configuration for their own use casescenario, (iii) an evaluation of the round-trip time feature's potential toreplace the IP address for enhanced user privacy, and (iv) a synthesized RBAdata set to reproduce this research and to foster future RBA research. Ourresults provide insights on selecting an optimized RBA configuration so thatusers profit from RBA after just a few logins. The open data set enablesresearchers to study, test, and improve RBA for widespread deployment in thewild.

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service