Security and Game Theory - Algorithms, Deployed Systems, Lessons Learned

By Milind Tambe. 2011

670 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability

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Citations in the corpus

Towards the Science of Security and Privacy in Machine Learning (2016, background): “Stackelberg games also allow for scalability compared to the corresponding simultaneous move game [90], [91].”
Randomized Prediction Games for Adversarial Machine Learning (2016, methodology): “, police officers for airport security and protection mechanisms for network security [51]–[53].”
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016, background): “Most of these real-world games involve decision making with imperfect information and high-dimensional information state spaces.”
SoK: Security and Privacy in Machine Learning (2018, background): “n this paper the classiﬁer) and the adversary responds optimally (choose a least cost evasion). Stackelberg games also allow for scalability compared to the corresponding simultaneous move game [90], [91]. Bruckner et al. [92] ﬁrst recognized that test data manipulation can be seen in the PAC framew”
Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games (2015, background): “Strong strategies are useful not only as research benchmarks and opponents in digital entertainment, but they are becoming increasingly important also in real world applications, such as critical infrastructure security [36].”
Deep Reinforcement Learning for Green Security Games with Real-Time Information (2018, methodology): “Most of the literature on SSG have neglected real-time information, with only a few exceptions (Zhang et al. 2014) that are not designed for green security domains.”
Heads-up limit hold’em poker is solved (2015, background): “More recently, there has been a surge in game-theoretic applications involving security, including systems being deployed for airport checkpoints, air marshal scheduling, and coast guard patrolling (46).”
A Concise Introduction to Decentralized POMDPs (2016, methodology): “Another interesting application is presented by Shieh et al. [2014], who apply Dec-MDPs in the context of security games which have been used for securing ports, airports and metro-rail systems [Tambe, 2011].”
An Exact Double-Oracle Algorithm for Zero-Sum Extensive-Form Games with Imperfect Information (2014, background): “The capabilities of these systems are based on a large amount of research in fast algorithms for security games (Tambe, 2011).”
Learning Adversary Behavior in Security Games: A PAC Model Perspective (2015, background): “. 1. INTRODUCTION Stackelberg Security Games (SSGs) are arguably the best example of the application of the Stackelberg game model in the real world. Indeed, numerous successful deployed applications [29] (LAX airport, US air marshal) and extensive research on related topics [5, 17, 11] provide evid”
Deep Learning for Predicting Human Strategic Behavior (2016, background): “Prominent examples of such systems include search engines, which use advertising auctions to generate a significant portion of their revenues and rely on game theoretic reasoning to analyze and optimize these mechanisms [6, 20]; spectrum auctions, which rely on game theoretic analysis to carefully d”
Protecting Moving Targets with Multiple Mobile Resources (2013, methodology): “The patrol schedules in these domains were previously created by hand; and hence suffer the drawbacks of hand-drawn patrols, including lack of randomness (in particular, informed randomness) and reliance on simple patrol patterns (Tambe, 2011), which we remedy in this paper.”
Solving Heads-Up Limit Texas Hold'em (2015, background): “Game theory provides a framework for thinking about decisions in the presence of other agents with applications in security [Tambe, 2011] and robust decision making [Chen and Bowling, 2012].”
Optimal Network Security Hardening Using Attack Graph Games (2015, methodology): “…attack graphs, (2) algorithms for analyzing these games, including fast methods based on MDPs for solving the attacker’s planning problem, (3) a case study analyzing the hardening solutions for sample networks, (4) empirical evaluation of the computational scalability and limitations of the…”