PAMMELA: Policy Administration Methodology using Machine Learning

Varun Gumma, Barsha Mitra, Soumyadeep Dey, Pratik Patel, Sourabh Suman, Saptarshi Das, Jaideep Vaidya

2022

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. For 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.

Paper Citation

in Harvard Style

Gumma V., Mitra B., Dey S., Patel P., Suman S., Das S. and Vaidya J. (2022). PAMMELA: Policy Administration Methodology using Machine Learning. In Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-590-6, pages 147-157. DOI: 10.5220/0011272400003283

in Bibtex Style

@conference{secrypt22,
author={Varun Gumma and Barsha Mitra and Soumyadeep Dey and Pratik Patel and Sourabh Suman and Saptarshi Das and Jaideep Vaidya},
title={PAMMELA: Policy Administration Methodology using Machine Learning},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2022},
pages={147-157},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011272400003283},
isbn={978-989-758-590-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - PAMMELA: Policy Administration Methodology using Machine Learning
SN - 978-989-758-590-6
AU - Gumma V.
AU - Mitra B.
AU - Dey S.
AU - Patel P.
AU - Suman S.
AU - Das S.
AU - Vaidya J.
PY - 2022
SP - 147
EP - 157
DO - 10.5220/0011272400003283