Multidefender Security Games
TL;DR: In this paper, the authors investigate security games with multiple defenders and develop a mixed-integer linear programming formulation to compute a defender's best response and approximate Nash equilibria of the game using this formulation.
read more
Abstract: Current Stackelberg security game models primarily focus on isolated systems in which only one defender is present, despite being part of a more complex system with multiple players. However, many real systems such as transportation networks and the power grid exhibit interdependencies among targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security scenarios, the authors investigate security games with multiple defenders. Unlike most prior analyses, they focus on situations in which each defender must protect multiple targets, so even a single defender's best response decision is, in general, nontrivial. Considering interdependencies among targets, the authors develop a novel mixed-integer linear programming formulation to compute a defender's best response, and approximate Nash equilibria of the game using this formulation. Their analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or underinvest in security.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Book
Adversarial Machine Learning
Yevgeniy Vorobeychik,Murat Kantarcioglu +1 more
- 08 Aug 2018
TL;DR: The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed in education and research.
169
A Unified Framework for Multistage and Multilevel Mixed Integer Linear Optimization.
TL;DR: In this paper, a unified framework for the study of multilevel mixed integer linear optimization problems and multistage stochastic MILO problems with recourse is introduced, which highlights the common mathematical structure of the two problems and allows for the development of a common algorithmic framework.
106
SHARE: A Stackelberg Honey-Based Adversarial Reasoning Engine
TL;DR: An adversarial foundation is developed for how NR-attackers will explore an enterprise network and how they will attack it, based on the concept of a system vulnerability dependency graph and a method for the attacker to use reinforcement learning when his or her activities are stopped by the defender is developed.
64
Optimal and Game-Theoretic Deployment of Security Investments in Interdependent Assets
Ashish R. Hota,Abraham A. Clements,Shreyas Sundaram,Saurabh Bagchi +3 more
- 02 Nov 2016
TL;DR: A game-theoretic framework to compute optimal and strategic security investments by multiple defenders and establishes the existence of a pure Nash equilibrium of the game between multiple defenders is introduced.
41
•Proceedings Article
Stackelberg Security Games with Multiple Uncoordinated Defenders
Jiarui Gan,Edith Elkind,Michael Wooldridge +2 more
- 09 Jul 2018
TL;DR: It is argued that an exact equilibrium may fail to exist, and, in fact, deciding whether it exists is NP-hard; however, under mild assumptions, every multi-defender security game admits an e-equilibrium for every e>0$, and the limit points corresponding to e\to 0$ can be efficiently approximated.
31
References
Maximizing the spread of influence through a social network
David Kempe,Jon Kleinberg,Éva Tardos +2 more
- 24 Aug 2003
TL;DR: An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Computing the optimal strategy to commit to
Vincent Conitzer,Tuomas Sandholm +1 more
- 11 Jun 2006
TL;DR: This paper studies how to compute optimal strategies to commit to under both commitment to pure strategies and commitment to mixed strategies, in both normal-form and Bayesian games.
Computing optimal randomized resource allocations for massive security games
Christopher Kiekintveld,Manish Jain,Jason Tsai,James Pita,Fernando Ordóñez,Milind Tambe +5 more
- 10 May 2009
TL;DR: A compact model of security games is used, which allows exponential improvements in both memory and runtime relative to the best known algorithms for solving general Stackelberg games and develops even faster algorithms for security games under payoff restrictions that are natural in many security domains.
Software Assistants for Randomized Patrol Planning for the LAX Airport Police and the Federal Air Marshal Service
Manish Jain,Jason Tsai,James Pita,Christopher Kiekintveld,Shyamsunder Rathi,Milind Tambe,Fernando Ordóòez +6 more
TL;DR: This paper describes two applications, ARMOR and IRIS, that assist security forces in randomizing their operations based on fast algorithms for solving large instances of Bayesian Stackelberg games.
271
PROTECT: a deployed game theoretic system to protect the ports of the United States
Eric Shieh,Bo An,Rong Yang,Milind Tambe,Craig Baldwin,Joseph DiRenzo,Ben Maule,Garrett Meyer +7 more
- 04 Jun 2012
TL;DR: PRETECT, a game-theoretic system deployed by the United States Coast Guard in the port of Boston for scheduling their patrols, is presented, a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior, the first real-world deployment of the QR model.