Authors:
Varun Gumma
1
;
Barsha Mitra
2
;
Soumyadeep Dey
3
;
Pratik Shashikantbhai Patel
2
;
Sourabh Suman
2
;
Saptarshi Das
4
and
Jaideep Vaidya
5
Affiliations:
1
Department of Computer Science and Engineering, IIT Madras, Chennai, India
;
2
Department of CSIS, BITS Pilani, Hyderabad Campus, Hyderabad, India
;
3
Microsoft India Development Center, India
;
4
JIS Institute of Advanced Studies and Research, JIS University, Kolkata, India
;
5
MSIS Department, Rutgers University, New Brunswick, NJ, U.S.A.
Keyword(s):
ABAC, Policy Administration, Policy Augmentation, Policy Adaptation, Supervised Learning.
Abstract:
In recent years, Attribute-Based Access Control (ABAC) has become quite popular and effective for enforcing access control in dynamic and collaborative environments. Implementation of ABAC requires the creation of a set of attribute-based rules which cumulatively form a policy. Designing an ABAC policy ab initio demands a substantial amount of effort from the system administrator. Moreover, organizational changes may necessitate the inclusion of new rules in an already deployed policy. In such a case, re-mining the entire ABAC policy requires a considerable amount of time and administrative effort. Instead, it is better to incrementally augment the policy. In this paper, we propose PAMMELA, a Policy Administration Methodology using Machine Learning to assist system administrators in creating new ABAC policies as well as augmenting existing policies. PAMMELA can generate a new policy for an organization by learning the rules of a policy currently enforced in a similar organization. Fo
r policy augmentation, new rules are inferred based on the knowledge gathered from the existing rules. A detailed experimental evaluation shows that the proposed approach is both efficient and effective.
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