$$\mathsf {QFlip}$$ : An Adaptive Reinforcement Learning Strategy for the $$\mathsf {FlipIt}$$ Security Game

DOI10.1007/978-3-030-32430-8_22zbMath1440.68037arXiv1906.11938OpenAlexW3106042141MaRDI QIDQ3297674

Alina Oprea, Lisa Oakley

Publication date: 20 July 2020

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1906.11938

zbMATH Keywords

Markov decision processesonline learningreinforcement learningadaptive strategiessecurity gamesFlipIt

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Applications of game theory (91A80) Computer security (68M25)

Related Items (1)

Deep reinforcement learning for \textsf{FlipIt} security game

Cites Work

• \texttt{FlipIt}: the game of ``stealthy takeover
• A Stackelberg game and Markov modeling of moving target defense
• FlipThem: Modeling Targeted Attacks with FlipIt for Multiple Resources
• Mitigating Covert Compromises
• Mitigation of Targeted and Non-targeted Covert Attacks as a Timing Game
• Are We Compromised? Modelling Security Assessment Games
• Defending against the Unknown Enemy: Applying FlipIt to System Security

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