Hau Chan's PhD Thesis Defense

Game-Theoretic Models for Interdependent Security: Modeling, Computing, and Learning
Due to an increase number of attacks by hackers and terrorists, there has been quite a bit of recent research activity in the general area of game-theoretic models for terrorism settings that aim to understand the behavior of the attackers and the attackers’ targets. My thesis is centered on introducing, studying, and applying several game-theoretic models to security.

In particular, my doctoral thesis consists of the following components: (1) designing increasingly more realistic variants of defense games; (2) studying computational questions in defense games such as equilibria computation and computational implications of equilibria characterizations, (3) designing efficient algorithms and effective heuristics for defense problems; and (4) designing and applying new machine learning techniques to estimate game model parameters from behavioral data.

In my doctoral thesis defense, I will present *interdependent defense (IDD) games*, a computational-game-theoretic framework for settings of interdependent security to study scenarios of multiple-defenders vs. a single-attacker in a network. In addition, I will discuss some computational aspects of computing Nash equilibria (NE) in our games. Finally, I will illustrate machine-learning techniques that I designed to estimate the parameters and structure of generalized interdependent security (IDS) games, a subclass of IDD games in which the attacker is not strategic, using the flu vaccination data from Centers for Disease Control and Prevention.

Dissertation Adviser: Luis E. Ortiz, Assistant Prof.
Committee Chairperson: Jie Gao, Associate Prof.
Other Committee Members:
* Jing Chen, Assistant Prof.
* Vincent Conitzer, Sally Dalton Robinson Professor of Computer Science and Professor of Economics, Duke University