Computational Game Theory for Security and Sustainability

Security is a critical concern around the world that arises in protecting our ports, airports, transportation and other critical national infrastructure from adversaries, in protecting our wildlife and forests from poachers and smugglers, and in curtailing the illegal flow of weapons, drugs and money; and it arises in problems ranging from physical to cyber-physical systems. In all of these problems, we have limited security resources which prevent full security coverage at all times; instead, security resources must be deployed intelligently taking into account differences in priorities of targets requiring security coverage, the responses of the attackers to the security posture, and potential uncertainty over the types, capabilities, knowledge and priorities of attackers faced. Game theory, which studies interactions among multiple selfinterested agents, is well-suited to the adversarial reasoning required for security resource allocation and scheduling problems. Casting the problem as a Bayesian Stackelberg game, we have developed new algorithms for efficiently solving such games that provide randomized patrolling or inspection strategies. These algorithms have led to some initial successes in this challenging problem arena, leading to advances over previous approaches in security scheduling and allocation, e.g., by addressing key weaknesses of predictability of human schedulers. These algorithms are now deployed in multiple applications: ARMOR has been deployed at the Los Angeles International Airport (LAX) since 2007 to randomize checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals [17]; IRIS, a game-theoretic scheduler for randomized deployment of the US Federal Air Marshals (FAMS) requiring significant scaleup in underlying algorithms, has been in use since 2009 [17]; PROTECT, which schedules the US Coast Guard’s randomized patrolling of ports using a new set of algorithms based on modeling bounded-rational human attackers, has been deployed in the

[1]  R. McKelvey,et al.  Quantal Response Equilibria for Normal Form Games , 1995 .

[2]  Amos Azaria,et al.  Analyzing the Effectiveness of Adversary Modeling in Security Games , 2013, AAAI.

[3]  Nicola Gatti,et al.  Game Theoretical Insights in Strategic Patrolling: Model and Algorithm in Normal-Form , 2008, ECAI.

[4]  T. Sandler,et al.  Terrorism & Game Theory , 2003 .

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

[6]  Manish Jain,et al.  Security Games with Arbitrary Schedules: A Branch and Price Approach , 2010, AAAI.

[7]  Vincent Conitzer,et al.  Security scheduling for real-world networks , 2013, AAMAS.

[8]  Sarit Kraus,et al.  Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games , 2008, AAMAS.

[9]  Milind Tambe,et al.  Bayesian Security Games for Controlling Contagion , 2013, 2013 International Conference on Social Computing.

[10]  B. Stengel,et al.  Leadership with commitment to mixed strategies , 2004 .

[11]  Vincent Conitzer,et al.  Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games , 2010, AAAI.

[12]  Milind Tambe,et al.  GUARDS: game theoretic security allocation on a national scale , 2011, AAMAS.

[13]  Branislav Bosanský,et al.  Game-theoretic resource allocation for malicious packet detection in computer networks , 2012, AAMAS.

[14]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[15]  Milind Tambe,et al.  A unified method for handling discrete and continuous uncertainty in Bayesian Stackelberg games , 2012, AAMAS.

[16]  Manish Jain,et al.  Software Assistants for Randomized Patrol Planning for the LAX Airport Police and the Federal Air Marshal Service , 2010, Interfaces.

[17]  Milind Tambe,et al.  Modeling Crime Diffusion and Crime Suppression on Transportation Networks: An Initial Report , 2013, AAAI Fall Symposia.

[18]  Bernhard von Stengel,et al.  Chapter 51 Inspection games , 2002 .

[19]  Nicholas J. Howard,et al.  Finding optimal strategies for influencing social networks in two player games , 2010 .

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

[21]  H. Stackelberg,et al.  Marktform und Gleichgewicht , 1935 .

[22]  Milind Tambe,et al.  Security and Game Theory: Evaluating Deployed Decision-Support Systems for Security: Challenges, Analysis, and Approaches , 2011 .

[23]  Bo An,et al.  Multi-objective optimization for security games , 2012, AAMAS.

[24]  Milind Tambe,et al.  TRUSTS: Scheduling Randomized Patrols for Fare Inspection in Transit Systems , 2012, IAAI.

[25]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[26]  G. Leitmann On generalized Stackelberg strategies , 1978 .

[27]  Manish Jain,et al.  Computing optimal randomized resource allocations for massive security games , 2009, AAMAS 2009.

[28]  Gerald G. Brown,et al.  Defending Critical Infrastructure , 2006, Interfaces.

[29]  Milind Tambe,et al.  Monotonic Maximin: A Robust Stackelberg Solution against Boundedly Rational Followers , 2013, GameSec.

[30]  Milind Tambe,et al.  Optimal patrol strategy for protecting moving targets with multiple mobile resources , 2013, AAMAS.

[31]  Jeannette M. Wing,et al.  Game strategies in network security , 2005, International Journal of Information Security.

[32]  Manish Jain,et al.  Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty , 2011, AAAI.

[33]  Aranyak Mehta,et al.  Playing large games using simple strategies , 2003, EC '03.

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

[35]  Bo An,et al.  PROTECT: a deployed game theoretic system to protect the ports of the United States , 2012, AAMAS.

[36]  A. Haurie,et al.  Sequential Stackelberg equilibria in two-person games , 1985 .

[37]  Sarit Kraus,et al.  Game-theoretic randomization for security patrolling with dynamic execution uncertainty , 2013, AAMAS.

[38]  Rong Yang,et al.  Improving Resource Allocation Strategy against Human Adversaries in Security Games , 2011, IJCAI.

[39]  Rong Yang,et al.  Challenges in Patrolling to Maximize Pristine Forest Area (Position Paper) , 2012, AAAI Spring Symposium: Game Theory for Security, Sustainability, and Health.

[40]  Bo An,et al.  Refinement of Strong Stackelberg Equilibria in Security Games , 2011, AAAI.

[41]  Milind Tambe,et al.  Security Games for Controlling Contagion , 2012, AAAI.

[42]  Rong Yang,et al.  A robust approach to addressing human adversaries in security games , 2012, AAMAS.

[43]  Vincent Conitzer,et al.  A double oracle algorithm for zero-sum security games on graphs , 2011, AAMAS.