Open AccessPosted Content
Multidefender Security Games
TL;DR: In this paper, the authors investigate game theoretic models of security games with multiple defenders and find that defenders have the incentive to over-protect targets, at times significantly, while the price of anarchy is unbounded.
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Abstract: Stackelberg security game models and associated computational tools have seen deployment in a number of high-consequence security settings, such as LAX canine patrols and Federal Air Marshal Service. These models focus on isolated systems with only one defender, despite being part of a more complex system with multiple players. Furthermore, many real systems such as transportation networks and the power grid exhibit interdependencies between targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security, we investigate game theoretic models of security games with multiple defenders. Unlike most prior analysis, we focus on the situations in which each defender must protect multiple targets, so that even a single defender's best response decision is, in general, highly non-trivial. We start with an analytical investigation of multidefender security games with independent targets, offering an equilibrium and price-of-anarchy analysis of three models with increasing generality. In all models, we find that defenders have the incentive to over-protect targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Considering interdependencies among targets, we develop a novel mixed-integer linear programming formulation to compute a defender's best response, and make use of this formulation in approximating Nash equilibria of the game. We apply this approach towards computational strategic analysis of several models of networks representing interdependencies, including real-world power networks. Our analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or under-invest in security.
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Citations
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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
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
A Game-Theoretic Framework for Securing Interdependent Assets in Networks
Ashish R. Hota,Abraham A. Clements,Saurabh Bagchi,Shreyas Sundaram +3 more
- 01 Jan 2018
TL;DR: A general game-theoretic framework to model the security investments of resource-constrained stakeholders against targeted attacks and how this framework can be applied to determine deployment of moving target defense (MTD) in networks is developed.
17
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Adversarial Regression with Multiple Learners
TL;DR: In this article, the authors study the problem of adversarial linear regression with multiple learners and approximate the resulting game by exhibiting an upper bound on learner loss functions, and show that the game has a unique symmetric equilibrium.
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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.
Stochastic Network Interdiction
TL;DR: A stochastic version of the interdictor's problem: Minimize the expected maximum flow through the network when interdiction successes are binary random variables is formulated and solved.
401
Stackelberg vs. Nash in security games: an extended investigation of interchangeability, equivalence, and uniqueness
TL;DR: It is shown that the Nash equilibria in security games are interchangeable, thus alleviating the equilibrium selection problem and proposed an extensive-form game model that makes the defender's uncertainty about the attacker's ability to observe explicit.