A cyber-security problem is a conflict-resolution scenario that typically consists of a security system and at least two decision makers (e.g. attacker and defender) that can each have competing objectives. In this thesis, we are interested in cyber-security problems where one decision maker has superior or better information. Game theory is a well-established mathematical tool that can be used to analyze such problems and will be our tool of choice. In particular, we will formulate cyber-security problems as stochastic games with asymmetric information, where game-theoretic methods can then be applied to the problems to derive optimal policies for each decision maker. A severe limitation of considering optimal policies is that these policies are computationally prohibitive. We address the complexity issues by introducing methods, based on the ideas of model predictive control, to compute suboptimal polices. Specifically, we first prove that the methods generate suboptimal policies that have tight performance bounds. We then show that the suboptimal polices can be computed by solving a linear program online, and the complexity of the linear program remains constant with respect to the game length. Finally, we demonstrate how the suboptimal policy methods can be applied to cyber-security problems to reduce the computational complexity of forecasting cyber-attacks.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/50284 |
| Date | 13 January 2014 |
| Creators | Jones, Malachi G. |
| Contributors | Shamma, Jeff S. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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