Modeling the Dynamics of Compromised Networks

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Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the ... continued below

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PDF-file: 21 pages; size: 0.6 Mbytes

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Soper, B & Merl, D M September 12, 2011.

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Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.

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PDF-file: 21 pages; size: 0.6 Mbytes

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  • Report No.: LLNL-TR-498747
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/1026467 | External Link
  • Office of Scientific & Technical Information Report Number: 1026467
  • Archival Resource Key: ark:/67531/metadc843362

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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Creation Date

  • September 12, 2011

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

Description Last Updated

  • Nov. 29, 2016, 8:22 p.m.

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Soper, B & Merl, D M. Modeling the Dynamics of Compromised Networks, report, September 12, 2011; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc843362/: accessed October 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.