A Security Domain Model for Implementing Trusted Subject Behaviors

Abstract

Within a multilevel secure (MLS) system, trusted subjects are granted privileges to perform operations that are not possible by ordinary subjects controlled by mandatory access control (MAC) policy enforcement mechanisms. These subjects are trusted not to conduct malicious activity or degrade system security. The authors present a formal definition for trusted subject behaviors that depends upon a representation of information flow and control dependencies generated during a program execution. They describe a security Domain Model (DM) designed in the Alloy specification language for conducting static analysis of programs to identify illicit information flows and access control flaws and covert channel vulnerabilities. The DM is compiled from a representation of a target program, written in an intermediate Implementation Modeling Language (IML), and a specification of the security policy written in Alloy. The Alloy Analyzer tool is used to perform static analysis of the DM to detect potential security policy violations in the target program. In particular, since the operating system upon which the trusted subject runs has limited ability to control its actions, static analysis of trusted subject operations can contribute to the security of the system.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2008
Accession Number
ADA512133

Entities

People

  • Alan Shaffer
  • Cynthia E. Irvine
  • Mikhail I. Auguston
  • Timothy E. Levin

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Analyzers
  • Computer Access Control
  • Computer Programming
  • Computer Science
  • Computers
  • Computing System Architectures
  • Database Management Systems
  • Filters
  • High Level Languages
  • Information Processing
  • Language
  • Mathematical Analysis
  • Multiple Access
  • Operating Systems
  • Programming Languages
  • Security
  • Specifications

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Cybersecurity.
  • Library and Information Science