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Decision Support for Mission-Centric Cyber Defence

Published: 26 August 2019 Publication History

Abstract

In this paper, we propose a novel approach to enterprise mission modeling and mission-centric decision support for cybersecurity operations. The goal of the decision support analytical process is to suggest an effective response for an ongoing attack endangering established mission security requirements. First, we propose an enterprise mission decomposition model to represent the requirements of the missions' processes and components on their confidentiality, integrity, availability. The model is illustrated in a real-world scenario of a medical information system. Second, we propose an analytical process that calculates mission resilience metrics using the attack graphs and Bayesian network reasoning. The process is designed to help cybersecurity operations teams in understanding the complexity of a situation and decision making concerning requirements on enterprise missions.

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ARES '19: Proceedings of the 14th International Conference on Availability, Reliability and Security
August 2019
979 pages
ISBN:9781450371643
DOI:10.1145/3339252
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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2019

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Author Tags

  1. Attack graph
  2. Bayesian network
  3. Cyber situational awareness
  4. Decision support
  5. Mission resilience

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ARES '19

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Overall Acceptance Rate 228 of 451 submissions, 51%

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