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Risk-based authentication (RBA) aims to strengthen password-basedauthentication rather than replacing it. RBA does this by monitoring andrecording additional features during the login process. If feature values atlogin time differ significantly from those observed before, RBA requests anadditional proof of identification. Although RBA is recommended in the NISTdigital identity guidelines, it has so far been used almost exclusively bymajor online services. This is partly due to a lack of open knowledge andimplementations that would allow any service provider to roll out RBAprotection to its users.  To close this gap, we provide a first in-depth analysis of RBAcharacteristics in a practical deployment. We observed N=780 users with 247unique features on a real-world online service for over 1.8 years. Based on ourcollected data set, we provide (i) a behavior analysis of two RBAimplementations that were apparently used by major online services in the wild,(ii) a benchmark of the features to extract a subset that is most suitable forRBA use, (iii) a new feature that has not been used in RBA before, and (iv)factors which have a significant effect on RBA performance. Our results showthat RBA needs to be carefully tailored to each online service, as even smallconfiguration adjustments can greatly impact RBA's security and usabilityproperties. We provide insights on the selection of features, their weightings,and the risk classification in order to benefit from RBA after a minimum numberof login attempts.

What's in Score for Website Users: A Data-driven Long-term Study on Risk-based Authentication Characteristics