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Issue title: Selected Papers From ESORICS 2021
Guest editors: Elisa Bertino, Haya Shulman and Michael Waidner
Article type: Research Article
Authors: David, Liron; * | Wool, Avishai
Affiliations: School of Electrical Engineering, Tel Aviv University, Israel
Correspondence: [*] Corresponding author. E-mail: lirondavid@gmail.com.
Note: [1] This paper is an extended and revised version of a paper presented at ESORICS 2021.
Abstract: Human-chosen passwords are the dominant form of authentication systems. Passwords strength estimators are used to help users avoid picking weak passwords by predicting how many attempts a password cracker would need until it finds a given password. In this paper we propose a novel password strength estimator, called PESrank, which accurately models the behavior of a powerful password cracker. PESrank calculates the rank of a given password in an optimal descending order of likelihood. PESrank estimates a given password’s rank in fractions of a second – without actually enumerating the passwords – so it is practical for online use. It also has a training time that is drastically shorter than previous methods. Moreover, PESrank is efficiently tweakable to allow model personalization in fractions of a second, without the need to retrain the model; and it is explainable: it is able to provide information on why the password has its calculated rank, and gives the user insight on how to pick a better password. We implemented PESrank in Python and conducted an extensive evaluation study of it. We also integrated it into the registration page of a course at our university. Even with a model based on 905 million passwords, the response time was well under 1 second, with up to a 1-bit accuracy margin between the upper bound and the lower bound on the rank.
Keywords: Password strength estimator, rank estimation, side-channel attack
DOI: 10.3233/JCS-210166
Journal: Journal of Computer Security, vol. 30, no. 6, pp. 877-901, 2022
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