Defending Against Opportunistic Criminals: New Game-Theoretic Frameworks and Algorithms

This paper introduces a new game-theoretic framework and algorithms for addressing opportunistic crime. The Stackelberg Security Game (SSG), which models highly strategic and resourceful adversaries, has become an important computational framework within multiagent systems. Unfortunately, SSG is ill-suited as a framework for handling opportunistic crimes, which are committed by criminals who are less strategic in planning attacks and more flexible in executing them than SSG assumes. Yet, opportunistic crime is what is commonly seen in most urban settings.We therefore introduce the Opportunistic Security Game (OSG), a computational framework to recommend deployment strategies for defenders to control opportunistic crimes. Our first contribution in OSG is a novel model for opportunistic adversaries, who (i) opportunistically and repeatedly seek targets; (ii) react to real-time information at execution time rather than planning attacks in advance; and (iii) have limited observation of defender strategies. Our second contribution to OSG is a new exact algorithm EOSG to optimize defender strategies given our opportunistic adversaries. Our third contribution is the development of a fast heuristic algorithm to solve large-scale OSG problems, exploiting a compact representation.We use urban transportation systems as a critical motivating domain, and provide detailed experimental results based on a real-world system.

[1]  R. Clarke,et al.  UNDERSTANDING CRIME DISPLACEMENT: AN APPLICATION OF RATIONAL CHOICE THEORY , 1987 .

[2]  P. Brantingham,et al.  Criminality of place , 1995 .

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

[4]  Xavier Boyen,et al.  Tractable Inference for Complex Stochastic Processes , 1998, UAI.

[5]  S. Sastry,et al.  Probabilistic pursuit-evasion games: a one-step Nash approach , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[6]  R. Liggett,et al.  The Geography of Transit Crime , 2002 .

[7]  Kelly E. See,et al.  Between ignorance and truth: Partition dependence and learning in judgment under uncertainty. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[8]  Jerry H. Ratcliffe,et al.  A Temporal Constraint Theory to Explain Opportunity-Based Spatial Offending Patterns , 2006 .

[9]  P. Brantingham,et al.  Offender Mobility and Crime Pattern Formation from First Principles , 2008 .

[10]  Andrea L. Bertozzi,et al.  c ○ World Scientific Publishing Company A STATISTICAL MODEL OF CRIMINAL BEHAVIOR , 2008 .

[11]  Tansu Alpcan,et al.  Stochastic games for security in networks with interdependent nodes , 2009, 2009 International Conference on Game Theory for Networks.

[12]  Milind Tambe,et al.  Effective solutions for real-world Stackelberg games: when agents must deal with human uncertainties , 2009, AAMAS 2009.

[13]  Nicola Basilico,et al.  Extending Algorithms for Mobile Robot Patrolling in the Presence of Adversaries to More Realistic Settings , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

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

[15]  Quanyan Zhu,et al.  Dynamic policy-based IDS configuration , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[16]  John S. Baras,et al.  Decision and Game Theory for Security , 2010, Lecture Notes in Computer Science.

[17]  Nicola Basilico,et al.  Automated Abstractions for Patrolling Security Games , 2011, AAAI.

[18]  Vincent Conitzer,et al.  Computing Optimal Strategies to Commit to in Stochastic Games , 2012, AAAI.

[19]  Quanyan Zhu,et al.  Deceptive Routing in Relay Networks , 2012, GameSec.

[20]  Vincent Conitzer,et al.  Computing Stackelberg strategies in stochastic games , 2012, SECO.

[21]  Bo An,et al.  Adversarial patrolling games , 2012, AAMAS.

[22]  Manish Jain,et al.  Game theory for security: Key algorithmic principles, deployed systems, lessons learned , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[23]  Quanyan Zhu,et al.  Deceptive routing games , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[24]  Zhi Yuan,et al.  Scalable Randomized Patrolling for Securing Rapid Transit Networks , 2013, IAAI.

[25]  Quanyan Zhu,et al.  Game theory meets network security and privacy , 2013, CSUR.

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

[27]  Milind Tambe,et al.  Online planning for optimal protector strategies in resource conservation games , 2014, AAMAS.

[28]  Joseph R. Zipkin,et al.  COPS ON THE DOTS IN A MATHEMATICAL MODEL OF URBAN CRIME AND POLICE RESPONSE , 2014 .

[29]  Quanyan Zhu,et al.  Decision and Game Theory for Security , 2016, Lecture Notes in Computer Science.

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