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PAMMELA: Policy Administration Methodology using Machine Learning.

Varun Gumma¹, Barsha Mitra², Soumyadeep Dey³

¹Department of Computer Science and Engineering, IIT Madras, Chennai, India.

SECRYPT ... : Proceedings of the International Conference on Security and Cryptography. International Conference on Security and Cryptography

|December 23, 2022

Summary

This summary is machine-generated.

PAMMELA uses machine learning to help administrators create and update Attribute-Based Access Control (ABAC) policies efficiently. This reduces the effort needed for policy design and incremental updates in dynamic environments.

Keywords:

ABAC Policy Adaptation Policy Administration Policy Augmentation Supervised Learning

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Area of Science:

Computer Science
Information Security
Machine Learning

Background:

Attribute-Based Access Control (ABAC) is crucial for dynamic environments but designing and updating policies is labor-intensive.
Current methods for policy modification require significant administrative effort and time.

Purpose of the Study:

To introduce PAMMELA, a novel methodology for automating ABAC policy creation and augmentation.
To assist system administrators in managing complex access control policies more effectively.

Main Methods:

PAMMELA employs machine learning to learn rules from existing policies in similar organizations for new policy generation.
For policy augmentation, new rules are inferred by analyzing existing policy knowledge.

Main Results:

Experimental evaluations demonstrate that PAMMELA is both efficient and effective in its operations.
The approach successfully reduces the administrative burden associated with ABAC policy management.

Conclusions:

PAMMELA offers a significant improvement over traditional methods for ABAC policy administration.
The proposed machine learning-based methodology streamlines policy creation and updates, enhancing system security and manageability.