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Decision Model for the Security and Utility Risk Evaluation (SURE) Framework

Published: 29 January 2019 Publication History

Abstract

The Security and Utility Risk Evaluation (SURE) framework is a framework for specifying and calculating risk to enable dynamic and autonomous decisions about cyber security and utility risk in generic computer-based systems. The SURE framework's decision model provides the ability to select between multiple alternative mitigation strategies in order to optimise security and utility risk during the operation of a system. This paper presents the decision model of the SURE framework and an example illustrating how the decision model operates in a mobile networking scenario. The example shows that the SURE framework's decision model enables a better fit than existing security decision models between the context of the requested action, security and utility requirements and the selected mitigation strategy, giving greater flexibility to both policy makers and users.

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Published In

cover image ACM Other conferences
ACSW '19: Proceedings of the Australasian Computer Science Week Multiconference
January 2019
486 pages
ISBN:9781450366038
DOI:10.1145/3290688
Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

In-Cooperation

  • CORE - Computing Research and Education
  • Macquarie University-Sydney

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 January 2019

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Author Tags

  1. Cyber security
  2. mitigation strategy
  3. operational requirement
  4. risk
  5. trade off
  6. utility

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  • Research-article
  • Research
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ACSW 2019
ACSW 2019: Australasian Computer Science Week 2019
January 29 - 31, 2019
NSW, Sydney, Australia

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ACSW '19 Paper Acceptance Rate 61 of 141 submissions, 43%;
Overall Acceptance Rate 61 of 141 submissions, 43%

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