Playing Adaptively Against Stealthy Opponents: A Reinforcement Learning Strategy for the FlipIt Security Game

Lisa Oakley
Lisa Oakley

CoRR, 2019.

Cited by: 0|Bibtex|Views31
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Other Links: dblp.uni-trier.de|arxiv.org

Abstract:

A rise in Advanced Persistant Threats (APTs) has introduced a need for robustness against long-running, stealthy attacks which circumvent existing cryptographic security guarantees. FlipIt is a security game that models the attacker-defender interactions in advanced scenarios such as APTs. Previous work analyzed extensively non-adaptive...More

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