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Policy models to protect resource retrieval

Published: 25 June 2014 Publication History

Abstract

Processes need a variety of resources from their operating environment in order to run properly, but adversary may control the inputs to resource retrieval or the end resource itself, leading to a variety of vulnerabilities. Conventional access control methods are not suitable to prevent such vulnerabilities because they use one set of permissions for all system call invocations. In this paper, we define a novel policy model for describing when resource retrievals are unsafe, so they can be blocked. This model highlights two contributions: (1) the explicit definition of adversary models as adversarial roles, which list the permissions that dictate whether one subject is an adversary of another, and (2) the application of data-flow to determine the adversary control of the names used to retrieve resources. An evaluation using multiple adversary models shows that data-flow is necessary to authorize resource retrieval in over 90% of system calls. By making adversary models and the adversary accessibility of all aspects of resource retrieval explicit, we can block resource access attacks system-wide.

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cover image ACM Conferences
SACMAT '14: Proceedings of the 19th ACM symposium on Access control models and technologies
June 2014
234 pages
ISBN:9781450329392
DOI:10.1145/2613087
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 June 2014

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  1. protection
  2. resource access attacks

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SACMAT '14 Paper Acceptance Rate 17 of 58 submissions, 29%;
Overall Acceptance Rate 177 of 597 submissions, 30%

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