Security is a critical concern around the world, whether it is the challenge of protecting ports, airports and other critical national infrastructure, or protecting wildlife/forests and fisheries, or suppressing crime in urban areas. In many of these cases, limited security resources prevent full security coverage at all times. Instead, these limited resources must be allocated and scheduled efficiently, avoiding predictability, while simultaneously taking into account an adversary’s response to the security coverage, the adversary’s preferences and potential uncertainty over such preferences and capabilities.

Computational game theory can help us build decision-aids for such efficient security resource allocation. Indeed, casting the security allocation problem as a Bayesian Stackelberg game, we have developed new algorithms that are deployed over multiple years in multiple applications:

  1. PROTECT for the US Coast Guard
  2. TRUSTS for the Los Angeles Sheriff’s Department
  3. IRIS for the Federal Air Marshal’s Service
  4. ARMOR for Los Angeles Airport Police
  5. GUARDS for the Transportation Security Administration
  6. DARMS for the Transportation Security Administration

Fundamentally, we are focused on the research challenges in these efforts, marrying these applications with research on topics such as (i) fast algorithms for solving massive-scale games; (ii) behavioral game theory research for addressing human adversaries who may act with bounded rationality and imperfect observations; (iii) understanding the impact of players’ limited observations on solution approaches adopted. We list the main research papers below and also some of our project application areas.

PROTECT: Application of game theory for US Coast Guard Patrols

Milind Tambe provides an overview of security research projects

Prof. Milind Tambe talks about Game Theory for Security

Security Games

Security game applications deployed and tested

Gratefully acknowledge the support of:


Current Projects
  • Dynamic Aviation Risk Management Solution (DARMS)

    The objective of DARMS is to unify, quantify, and integrate information across the aviation sector in order to comprehensively assess risk on an individual, on a per flight basis. DARMS will integrate information on passengers, checked baggage and cargo, aircraft operators and airports and airport perimeters.

  • Opportunistic crime security game and machine learning for crime prediction

    Inspired by modern criminological theory, we introduce the Opportunistic Security Game (OSG), a new computational framework for generating defender strategies to mitigate opportunistic criminals. Furthermore, we applying machine learning to learn a model of criminal behavior.

  • The Power of Information in Security Games

    Information – i.e., who knows what knowledge regarding a game – has a profound influence on the equilibrium outcome of the game. From the informational perspective, defense is all about shaping the attacker’s belief regarding the protection of targets, and randomly allocating physical resources is just one way to achieve this. The attacker’s belief is also largely affected by the information available to him, such as payoff structures, effectiveness of physical resources, vulnerability of targets, defense deployments, etc. Crucially, the defender usually has more knowledge regarding these aspects than the attacker. The central question we aim to answer in this project is, can the defender make use of such knowledge to increase the defensive effects, and if so, how she can do it optimally?

Past Projects

Recent news about Game Theory for Security

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