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Using Machine Learning in Adversarial Environments

Davis, Warren L.; Dunlavy, Daniel M.; Vorobeychik, Yevgeniy; Butler, Karin; Forsythe, Chris; Letter, Matthew; Murchison, Nicole; Nauer, Kevin

Cyber defense is an asymmetric battle today. We need to understand better what options are available for providing defenders with possible advantages. Our project combines machine learning, optimization, and game theory to obscure our defensive posture from the information the adversaries are able to observe. The main conceptual contribution of this research is to separate the problem of prediction, for which machine learning is used, and the problem of computing optimal operational decisions based on such predictions, coupled with a model of adversarial response. This research includes modeling of the attacker and defender, formulation of useful optimization models for studying adversarial interactions, and user studies to measure the impact of the modeling approaches in realistic settings.

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TYPE SAND Report YEAR 2016
DOIOSTI
Results 4476–4500 of 9,998
Results 4476–4500 of 9,998