ASASP: Automated symbolic analysis of security policies

Francesco Alberti, Alessandro Armando, Silvio Ranise

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe asasp, a symbolic reachability procedure for the analysis of administrative access control policies. The tool represents access policies and their administrative actions as formulae of the Bernays-Shönfinkel-Ramsey class and then uses a symbolic reachability procedure to solve security analysis problems. Checks for fix-point-reduced to satisfiability problems-are mechanized by Satisfiability Modulo Theories solving and Automated Theorem Proving. asasp has been successfully applied to the analysis of benchmark problems arising in (extensions of) the Role-Based Access Control model. Our tool shows better scalability than a state-of-the-art tool on a significant set of instances of these problems.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages26-33
Number of pages8
Volume6803 LNAI
DOIs
Publication statusPublished - 2011
Event23rd International Conference on Automated Deduction, CADE 23 - Wroclaw, Poland
Duration: Jul 31 2011Aug 5 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6803 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other23rd International Conference on Automated Deduction, CADE 23
CountryPoland
CityWroclaw
Period7/31/118/5/11

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Alberti, F., Armando, A., & Ranise, S. (2011). ASASP: Automated symbolic analysis of security policies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6803 LNAI, pp. 26-33). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6803 LNAI). https://doi.org/10.1007/978-3-642-22438-6_4