Pump up password security!

  • Risk-based authentication (RBA) aims to protect users against attacks involving stolen passwords. RBA monitors features during login, and requests re-authentication when feature values widely differ from those previously observed. It is recommended by various national security organizations, and users perceive it more usable than and equally secure to equivalent two-factor authentication. Despite that, RBA is still used by very few online services. Reasons for this include a lack of validated open resources on RBA properties, implementation, and configuration. This effectively hinders the RBA research, development, and adoption progress. To close this gap, we provide the first long-term RBA analysis on a real-world large-scale online service. We collected feature data of 3.3 million users and 31.3 million login attempts over more than 1 year. Based on the data, we provide (i) studies on RBA’s real-world characteristics plus its configurations and enhancements to balance usability, security, and privacy; (ii) a machine learning–based RBA parameter optimization method to support administrators finding an optimal configuration for their own use case scenario; (iii) an evaluation of the round-trip time feature’s potential to replace the IP address for enhanced user privacy; and (iv) a synthesized RBA dataset to reproduce this research and to foster future RBA research. Our results provide insights on selecting an optimized RBA configuration so that users profit from RBA after just a few logins. The open dataset enables researchers to study, test, and improve RBA for widespread deployment in the wild.

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Author:Stephan WieflingORCiDGND, Paul René JørgensenORCiDGND, Sigurd ThunemORCiDGND, Luigi Lo IaconoORCiDGND
Parent Title (English):ACM Transactions on privacy and security
Subtitle (English):Evaluating and enhancing risk-based authentication on a real-world large-scale online service
Publisher:Association for Computing Machinery
Place of publication:New York City, New York
Document Type:Article
Date of Publication (online):2024/03/01
Date of first Publication:2022/11/07
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Tag:Risk-based authentication; big data analysis; large-scale online services
Issue:1, Artikel 6
First Page:6-1
Last Page:6-36
Institutes/Facilities:Horst Görtz Institut für IT-Sicherheit
Dewey Decimal Classification:Allgemeines, Informatik, Informationswissenschaft / Informatik
open_access (DINI-Set):open_access
faculties:Fakultät für Informatik
Licence (English):License LogoCreative Commons - CC BY 4.0 - Attribution 4.0 International