End-to-End Game-Focused Learning of Adversary Behavior in Security Games

Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of learning a defense that generalizes well to a new set of targets with novel feature values and combinations. Traditionally, this problem has been addressed via a two-stage approach where an adversary model is trained to maximize predictive accuracy without considering the defender's optimization problem. We develop an end-to-end game-focused approach, where the adversary model is trained to maximize a surrogate for the defender's expected utility. We show both in theory and experimental results that our game-focused approach achieves higher defender expected utility than the two-stage alternative when there is limited data.Comment: Appeared at AAAI 202

Game-Theoretic Resource Allocation for Protecting Large Public Events

High profile large scale public events are attractive targets for terrorist attacks. The recent Boston Marathon bombings on April 15, 2013 have further emphasized the importance of protecting public events. The security challenge is exacerbated by the dynamic nature of such events: e.g., the impact of an attack at different locations changes over time as the Boston marathon participants and spectators move along the race track. In addition, the defender can relocate security resources among potential attack targets at any time and the attacker may act at any time during the event. This paper focuses on developing efficient patrolling algorithms for such dynamic domains with continuous strategy spaces for both the defender and the attacker. We aim at computing optimal pure defender strategies, since an attacker does not have an opportunity to learn and respond to mixed strategies due to the relative infrequency of such events. We propose SCOUT-A, which makes assumptions on relocation cost, exploits payoff representation and computes optimal solutions efficiently. We also propose SCOUT-C to compute the exact optimal defender strategy for general cases despite the continuous strategy spaces. SCOUT-C computes the optimal defender strategy by constructing an equivalent game with discrete defender strategy space, then solving the constructed game. Experimental results show that both SCOUT-A and SCOUT-C significantly outperform other existing strategies

Simulating resource allocation for protecting public events

With the rise in global terrorism, the need to ensure the safety of the public is greater than ever. Public events are very susceptible to terrorist attacks, although there are security resources that protect these event, there is the problem of allocating the resources effectively and efficiently. Many resource allocation algorithms has been proposed to resolve the problem. The objective of this project is to design and create a simulation model integrated with Geographic Information System (GIS) to simulate how systems behave in real-life using resource allocation algorithms and then use the data collected to evaluate the performance of these algorithms. For this project we designed the simulation model based on the article Game-theoretic Resource Allocation for Protecting Large Public Events" and test the model using the SCOUT-A algorithm proposed in the article. Repast Simphony is used to implement the simulation model and the model created is succesfully integrated with GIS, it is able to generate a 3D visualisation of geographic features based on real-world objects. Using Boston Marathon as an example to carried out simulations using SCOUT-A algorithm to allocated security resources, it is shown that SCOUT-A algorithm is more effective than general defense strategies.Bachelor of Engineering (Computer Science