U.S. flag   An unofficial archive of your favorite United States government website
Dot gov

Official websites do not use .rip
We are an unofficial archive, replace .rip by .gov in the URL to access the official website. Access our document index here.

Https

Secure websites use HTTPS
A lock (Dot gov) or https:// means you've safely connected to our website. Please do not share sensitive information with us.

This is an archive
(replace .gov by .rip)

Machine Learning for Access Control Policy Verification: NISTIR 8360 Published
September 16, 2021

Access control policy verification ensures that there are no faults within the policy that leak or block access privileges. As a software test, access control policy verification relies on methods such as model proof, data structure, system simulation, and test oracle to verify that the policy logic functions as expected. However, these methods have capability and performance issues related to inaccuracy and complexity limited by applied technologies. For instance, model proof, test oracle, and data structure methods initially assume that the policy under verification is faultless unless the policy model cannot hold for test cases. Thus, the challenge of the method is to compose test cases that can comprehensively discover all faults. Alternatively, a system simulation method requires translating the policy to a simulated system. The translation between systems may be difficult or impractical to implement if the policy logic is complicated or the number of policy rules is large.

NISTIR 8360, Machine Learning for Access Control Policy Verification, proposes an efficient and straightforward method for access control policy verification by applying a classification algorithm of machine learning, which does not require comprehensive test cases, oracle, or system translation but rather checks the logic of policy rules directly, making it more efficient and feasible compared to traditional methods. Ultimately, three general applications are provided: enhancement of existing verification methods, verification of access control policies with numerical attributes, and policy enforcement that can be supported by the proposed machine learning policy verification method.

Related Topics

Security and Privacy: access authorization, access control

Technologies: artificial intelligence

Created September 16, 2021