A generalized adversary decision algorithm and analytic solution methods for advise models
Ford, Michael
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https://hdl.handle.net/2142/31008
Description
Title
A generalized adversary decision algorithm and analytic solution methods for advise models
Author(s)
Ford, Michael
Issue Date
2012-05-22T00:21:15Z
Director of Research (if dissertation) or Advisor (if thesis)
Sanders, William H.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Security models
quantitative metrics
state space exploration
numerical techniques
Abstract
Quantitative security metrics are becoming increasingly important to system administrators. ADVISE generates quantitative security metrics by combining a system vulnerability graph with an adversary profile through an adversary decision algorithm. Previously, the decision algorithm placed restrictive assumptions on the adversary profile, and simulation was the sole solution method for ADVISE models. In this thesis, the decision algorithm is generalized while simultaneously improving its performance by incorporating theory from discrete-time Markov games. Furthermore, by exploring the state-space and generating the transition probability matrix, numerical solution methods may be applied to solve ADVISE models. Identifying key properties allows the models to be tested for compatibility with alternative solution methods from the literature, enabling additional metrics for ADVISE models. Finally, the performance of simulation is improved significantly by introducing decision caching. Together these accomplishments expand the number of quantitative security metrics and solution methods available to ADVISE models while lifting restrictions on the adversary profile and improving performance.
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