Deep reinforcement learning for \textsf{FlipIt} security game

DOI10.1007/978-3-030-93409-5_68OpenAlexW4205923038MaRDI QIDQ2086680

Laura Greige, Peter Chin

Publication date: 25 October 2022

Full work available at URL: https://arxiv.org/abs/2002.12909

zbMATH Keywords

game theory\textsf{FlipIt}cybersecurity gamesdeep Q-learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Game theory (91Axx)

Uses Software

• PyTorch
• Adam

Cites Work

• Unnamed Item
• \texttt{FlipIt}: the game of ``stealthy takeover
• Decision and game theory for security. First international conference, GameSec 2010, Berlin, Germany, November 22--23, 2010. Proceedings
• Interdependent security
• \({\mathcal Q}\)-learning
• FlipThem: Modeling Targeted Attacks with FlipIt for Multiple Resources
• Towards a Metric for Communication Network Vulnerability to Attacks: A Game Theoretic Approach
• Mitigating Covert Compromises
• Mitigation of Targeted and Non-targeted Covert Attacks as a Timing Game
• $$\mathsf {QFlip}$$ : An Adaptive Reinforcement Learning Strategy for the $$\mathsf {FlipIt}$$ Security Game
• Are We Compromised? Modelling Security Assessment Games
• A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

This page was built for publication: Deep reinforcement learning for \textsf{FlipIt} security game