Skeptic: Automatic, Justified and Privacy-Preserving Password Composition Policy Selection

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Johnson, SA (Author) Other; Ferreira, JF (Author) Other; Mendes, A (Author) Other; Cordry, J (Author) Other

Conference proceedings International

Title: ASIA CCS '20: The 15th ACM Asia Conference on Computer and Communications Security, Taipei, Taiwan, October 5-9, 2020

Pages: 101-115

15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020

5 October 2020 through 9 October 2020

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00S-GJP

DOI: 10.1145/3320269.3384762

Abstract (EN): The choice of password composition policy to enforce on a password-protected system represents a critical security decision, and has been shown to significantly affect the vulnerability of user-chosen passwords to guessing attacks. In practice, however, this choice is not usually rigorous or justifiable, with a tendency for system administrators to choose password composition policies based on intuition alone. In this work, we propose a novel methodology that draws on password probability distributions constructed from large sets of real-world password data which have been filtered according to various password composition policies. Password probabilities are then redistributed to simulate different user password reselection behaviours in order to automatically determine the password composition policy that will induce the distribution of user-chosen passwords with the greatest uniformity, a metric which we show to be a useful proxy to measure overall resistance to password guessing attacks. Further, we show that by fitting power-law equations to the password probability distributions we generate, we can justify our choice of password composition policy without any direct access to user password data. Finally, we present Skeptic - -a software toolkit that implements this methodology, including a DSL to enable system administrators with no background in password security to compare and rank password composition policies without resorting to expensive and time-consuming user studies. Drawing on 205,176,321 passwords across 3 datasets, we lend validity to our approach by demonstrating that the results we obtain align closely with findings from a previous empirical study into password composition policy effectiveness. © 2020 ACM.

Language: English

Type (Professor's evaluation): Scientific