Exploring Information Asymmetry in Two-Stage Security Games

Stackelberg security games have been widely deployed to protect real-world assets. The main solution concept there is the Strong Stackelberg Equilibrium (SSE), which optimizes the defender's random allocation of limited security resources. However, solely deploying the SSE mixed strategy has limitations. In the extreme case, there are security games in which the defender is able to defend all the assets "almost perfectly" at the SSE, but she still sustains significant loss. In this paper, we propose an approach for improving the defender's utility in such scenarios. Perhaps surprisingly, our approach is to strategically reveal to the attacker information about the sampled pure strategy. Specifically, we propose a two-stage security game model, where in the first stage the defender allocates resources and the attacker selects a target to attack, and in the second stage the defender strategically reveals local information about that target, potentially deterring the attacker's attack plan. We then study how the defender can play optimally in both stages. We show, theoretically and experimentally, that the two-stage security game model allows the defender to achieve strictly better utility than SSE.

[1]  Juliane Hahn,et al.  Security And Game Theory Algorithms Deployed Systems Lessons Learned , 2016 .

[2]  Milind Tambe,et al.  Security and Game Theory: IRIS – A Tool for Strategic Security Allocation in Transportation Networks , 2011, AAMAS 2011.

[3]  Paul R. Milgrom,et al.  A theory of auctions and competitive bidding , 1982 .

[4]  Ricardo Alonso,et al.  Persuading Voters , 2015 .

[5]  Gerald G. Brown,et al.  A Two-Sided Optimization for Theater Ballistic Missile Defense , 2005, Oper. Res..

[6]  Bo An,et al.  Security Games with Limited Surveillance , 2012, AAAI.

[7]  Paul Milgrom,et al.  Online Advertising: Heterogeneity and Conflation in Market Design , 2010 .

[8]  P. Milgrom What the Seller Wont Tell You: Persuasion and Disclosure in Markets , 2009 .

[9]  V. Bier,et al.  Reasons for Secrecy and Deception in Homeland‐Security Resource Allocation , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[10]  Emir Kamenica,et al.  Bayesian Persuasion , 2009 .

[11]  Yoav Shoham,et al.  Run the GAMUT: a comprehensive approach to evaluating game-theoretic algorithms , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[12]  Stephen Morris,et al.  Bayes Correlated Equilibrium and the Comparison of Information Structures , 2013 .

[13]  Nicola Basilico,et al.  Leader-follower strategies for robotic patrolling in environments with arbitrary topologies , 2009, AAMAS.

[14]  Yevgeniy Vorobeychik,et al.  Securing interdependent assets , 2012, Autonomous Agents and Multi-Agent Systems.

[15]  Vincent Conitzer,et al.  Computing the optimal strategy to commit to , 2006, EC '06.

[16]  Robert Powell,et al.  Allocating Defensive Resources with Private Information about Vulnerability , 2007, American Political Science Review.

[17]  Y. Vorobeychik,et al.  Optimal Deceptive Strategies in Security Games : A Preliminary Study , 2013 .

[18]  Vincent Conitzer,et al.  Solving Zero-Sum Security Games in Discretized Spatio-Temporal Domains , 2014, AAAI.

[19]  Ariel D. Procaccia,et al.  Lazy Defenders Are Almost Optimal against Diligent Attackers , 2014, AAAI.