Uncertainty in Artificial Intelligence
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Computing Optimal Security Strategies for Interdependent Assets
Joshua Letchford, Yevgeniy Vorobeychik
Abstract:
We introduce a novel framework for computing optimal randomized security policies in networked domains which extends previous approaches in several ways. First, we extend previous linear programming techniques for Stackelberg security games to incorporate benefits and costs of arbitrary security configurations on individual assets. Second, we offer a principled model of failure cascades that allows us to capture both the direct and indirect value of assets, and extend this model to capture uncertainty about the structure of the interdependency network. Third, we extend the linear programming formulation to account for exogenous (random) failures in addition to targeted attacks. The goal of our work is two-fold. First, we aim to develop techniques for computing optimal security strategies in realistic settings involving interdependent security. To this end, we evaluate the value of our technical contributions in comparison with previous approaches, and show that our approach yields much better defense policies and scales to realistic graphs. Second, our computational framework enables us to attain theoretical insights about security on networks. As an example, we study how allowing security to be endogenous impacts the relative resilience of different network topologies.
Keywords:
Pages: 459-468
PS Link:
PDF Link: /papers/12/p459-letchford.pdf
BibTex:
@INPROCEEDINGS{Letchford12,
AUTHOR = "Joshua Letchford and Yevgeniy Vorobeychik",
TITLE = "Computing Optimal Security Strategies for Interdependent Assets",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "459--468"
}


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