A learning-based approach for SELinux policy optimization with type mining

Authors:
Said Marouf;Doan Minh Phuong;Mohamed Shehab
Affiliations:
University of North Carolina at Charlotte;University of Engineering and Technology, VNU at Hanoi;University of North Carolina at Charlotte
Venue:
Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research
Year:
2010

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Abstract

One of the major steps towards enhancing the security of the Linux operating system was the introduction of Security Enhanced Linux (SELinux) [1], developed by the U.S. National Security Agency. SELinux is a kernel Linux Security Module (LSM) that adds Mandatory Access Control (MAC) to a regular Linux system with Discretionary Access Control (DAC) [2]. SELinux supports Type Enforcement (TE), Role Based Access Control (RBAC), and Multi-Level Security Levels (MLS).