Stackelberg Games for Adversarial Learning

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Adversarial machine learning concerns the situation where data miners face attacks fromactive adversaries. The interactions between the data miner and the adversary can bemodelled as a game between two players. While some game theoretic models assumethat the players act simultaneously, a perhaps more appropriate assumption in this project is that players can observe their opponents' actions before making their own decision. In the Stackelberg game model, players act sequentially, allowing for such an assumption. The first aim of this project is to develop a theoretical framework for a better understanding of Stackelberg adversarial learning problems; this step will then inform the process of constructing fast and accurate algorithms to solve them. Various scenarios will be considered, including situations where an adversary is a leader while the corresponding data miner is a follower and vice-versa. The theoretical framework and resulting solution algorithms will be applied to practical problems based on open-source data sets.