NDLI: Inferring access-control policy properties via machine learning

Content Provider	IEEE Xplore Digital Library
Author	Martin, E. Tao Xie
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., North Carolina State Univ., Raleigh, NC (Martin, E.)
Abstract	To ease the burden of implementing and maintaining access-control aspects in a system, a growing trend among developers is to write access-control policies in a specification language such as XACML and integrate the policies with applications through the use of a policy decision point (PDP). To assure that the specified polices reflect the expected ones, recent research has developed policy verification tools; however, their applications in practice are still limited, being constrained by the limited set of supported policy language features and the unavailability of policy properties. This paper presents a data-mining approach to the problem of verifying that expressed access-control policies reflect the true desires of the policy author. We developed a tool to investigate this approach by automatically generating requests, evaluating those requests to get responses, and applying machine learning on the request-response pairs to infer policy properties. These inferred properties facilitate the inspection of the policy behavior. We applied our tool on an access-control policy of a central grades repository system for a university. Our results show that machine learning algorithms can provide valuable insight into basic policy properties and help identify specific bug-exposing requests
Sponsorship	Office of the Dean of the Faculty of Sci. The Univ. of Western Ontario Office of the Vice President of Res. and Int. Relations Centre for Advanced Studies, IBM Toronto Lab
Starting Page	4
Ending Page	238
File Size	151466
Page Count	235
File Format	PDF
ISBN	0769525989
DOI	10.1109/POLICY.2006.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-05
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Machine learning algorithms Application software Computer science Specification languages Inspection Natural languages Conferences
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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