Who touched my mission: Towards probabilistic mission impact assessment

Xiaoyan Sun, Anoop Singhal, Peng Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSafeConfig 2015 - Proceedings of the 2015 Workshop on Automated Decision Making for Active Cyber Defense
PublisherAssociation for Computing Machinery, Inc
Pages21-26
Number of pages6
ISBN (Electronic)9781450338219
DOIs
StatePublished - Oct 12 2015
EventWorkshop on Automated Decision Making for Active Cyber Defense, SafeConfig 2015 - Denver, United States
Duration: Oct 12 2015 → …

Publication series

NameSafeConfig 2015 - Proceedings of the 2015 Workshop on Automated Decision Making for Active Cyber Defense

Other

OtherWorkshop on Automated Decision Making for Active Cyber Defense, SafeConfig 2015
Country/TerritoryUnited States
CityDenver
Period10/12/15 → …

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Artificial Intelligence

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