Security is a global concern and a unifying theme in various security projects is strategic reasoning where the mathematical framework of machine learning and game theory can be integrated
and applied. For example, in the environmental sustainability domain, the problem of protecting
endangered wildlife from attacks (i.e., poachers’ strikes) can be abstracted as a game between
defender(s) and attacker(s). Applying previous research on security games to sustainability domains (denoted as Green Security Games) introduce several novel challenges that I address in
my thesis to create computationally feasible and accurate algorithms in order to model complex
adversarial behavior based on real-world data and to generate optimal defender strategy.
My thesis provides four main contributions to the emerging body of research in using machine
learning and game theory framework for the fundamental challenges existing in the environmental sustainability domain, namely (i) novel spatio-temporal and uncertainty-aware machine
learning models for complex adversarial behavior based on the imperfect real-world data, (ii) the
first large-scale field test evaluation of the machine learning models in the adversarial settings
concerning the environmental sustainability, (iii) a novel multi-expert online learning model for
constrained patrol planning, and (iv) the first game theoretical model to generate optimal defender
strategy against collusive adversaries.
In regard to the first contribution, I developed bounded rationality models for adversaries
based on the real-world data that account for the naturally occurring uncertainty in past attack
evidence collected by defenders. To that end, I proposed two novel predictive behavioral models,
which I improved progressively. The second major contribution of my thesis is a large-scale field
test evaluation of the proposed adversarial behavior model beyond the laboratory. Particularly,
my thesis is motivated by the challenges in wildlife poaching, where I directed the defenders
(i.e., rangers) to the hotspots of adversaries that they would have missed. During these experiments across multiple vast national parks, several snares and snared animals were detected, and
poachers were arrested, potentially more wildlife saved. The algorithm I proposed, that combines
machine learning and game-theoretic patrol planning is planned to be deployed at 600 national
parks around the world in the near future to combat illegal poaching.
The third contribution in my thesis introduces a novel multi-expert online learning model for
constrained and randomized patrol planning, which benefits from several expert planners where
insufficient or imperfect historical records of past attacks are available to learn adversarial behavior. The final contribution of my thesis is developing an optimal solution against collusive
adversaries in security games assuming both rational and boundedly rational adversaries. I conducted human subject experiments on Amazon Mechanical Turk involving 700 human subjects
using a web-based game that simulates collusive security games.