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Who Touched My Mission: Towards Probabilistic Mission Impact Assessment

Published: 12 October 2015 Publication History

Abstract

Cyber attacks inevitably generate impacts towards relevant missions. However, concrete methods to accurately evaluate such impacts are rare. In this paper, we propose a probabilistic approach based on Bayesian networks for quantitative mission impact assessment. A System Object Dependency Graph (SODG) is first built to capture the intrusion propagation process at the low operating system level. On top of the SODG, a mission-task-asset (MTA) map can be established to associate the system objects with corresponding tasks and missions. Based on the MTA map, a Bayesian network can be constructed to leverage the collected intrusion evidence and infer the probabilities of tasks and missions being tainted. This approach is promising for effective quantitative mission impact assessment.

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Cited By

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  • (2024)iMIA: Interdependent Mission Impact Assessment Using Subjective Bayesian NetworksNOMS 2024-2024 IEEE Network Operations and Management Symposium10.1109/NOMS59830.2024.10575273(1-7)Online publication date: 6-May-2024
  • (2024)Cyber Key Terrain Identification Using Adjusted PageRank CentralityICT Systems Security and Privacy Protection10.1007/978-3-031-56326-3_21(293-306)Online publication date: 24-Apr-2024
  • (2022)CIIAProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507313(124-132)Online publication date: 25-Apr-2022
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    cover image ACM Conferences
    SafeConfig '15: Proceedings of the 2015 Workshop on Automated Decision Making for Active Cyber Defense
    October 2015
    112 pages
    ISBN:9781450338219
    DOI:10.1145/2809826
    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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    Publication History

    Published: 12 October 2015

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    Author Tags

    1. Bayesian network
    2. mission impact assessment
    3. system object dependency graph

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    SafeConfig '15 Paper Acceptance Rate 8 of 27 submissions, 30%;
    Overall Acceptance Rate 22 of 61 submissions, 36%

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    Cited By

    View all
    • (2024)iMIA: Interdependent Mission Impact Assessment Using Subjective Bayesian NetworksNOMS 2024-2024 IEEE Network Operations and Management Symposium10.1109/NOMS59830.2024.10575273(1-7)Online publication date: 6-May-2024
    • (2024)Cyber Key Terrain Identification Using Adjusted PageRank CentralityICT Systems Security and Privacy Protection10.1007/978-3-031-56326-3_21(293-306)Online publication date: 24-Apr-2024
    • (2022)CIIAProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507313(124-132)Online publication date: 25-Apr-2022
    • (2022)Mission‐centric decision support in cybersecurity via Bayesian Privilege Attack GraphEngineering Reports10.1002/eng2.125384:12Online publication date: 9-Jun-2022
    • (2021)Impact Propagation in Airport SystemsCyber-Physical Security for Critical Infrastructures Protection10.1007/978-3-030-69781-5_13(191-206)Online publication date: 18-Feb-2021
    • (2019)A New Bayesian Approach to Exploring Damaged Assets by Monitoring Mission Failures Caused by Undetected AttackG Protein-Coupled Receptor Signaling10.1007/978-3-030-17982-3_15(185-196)Online publication date: 12-Apr-2019
    • (2018)Mission-Centric Risk Assessment to Improve Cyber Situational AwarenessProceedings of the 13th International Conference on Availability, Reliability and Security10.1145/3230833.3233281(1-8)Online publication date: 27-Aug-2018
    • (2018)Assessing Attack Impact on Business Processes by Interconnecting Attack Graphs and Entity Dependency GraphsData and Applications Security and Privacy XXXII10.1007/978-3-319-95729-6_21(330-348)Online publication date: 10-Jul-2018

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