@mastersthesis{SonmezMaster_2008,
TITLE = {Learning Game Theoretic Model Parameters Applied to Adversarial Classi{fi}cation},
AUTHOR = {S{\"o}nmez, Orhan},
LANGUAGE = {eng},
SCHOOL = {Universit{\"a}t des Saarlandes},
ADDRESS = {Saarbr{\"u}cken},
YEAR = {2008},
DATE = {2008},
CONTENTS = {In most of the machine learning approaches, it is commonly assumed that the data is independent and identically distributed from a data distribution. However, this is not the actual case in the real world applications. Hence, a further assumption could be done about the type of noise over the data in order to correctly model the real world. However, in some application domains such as spam {fi}ltering, intrusion detection, fraud detection etc. this assumption does not hold as there exists an opponent adversary that reacts the {fi}ltering process of the classi{fi}er and modi{fi}es the upcoming data accordingly. Hence, the performance of the classi{fi}er degrades rapidly after its deployment with the counter actions of the adversary. When not assuming the independence of the data generation from the classi{fi}cation, arise a new problem, namely the adversarial learning problem. Now, the classi{fi}er should estimate the classi{fi}cation parameters with considering the presence of the opponent adversary and furthermore has to adapt itself to the activities of it. In order to solve this adversarial learning problem, a two-player game is de{fi}ned between the classi{fi}er and the adversary. Afterward, the game results are resolved for diﬀerent classi{fi}er losses such as adversary-aware and utilitybased classi{fi}er, and for diﬀerent adversarial strategies such as worst-case, goal-based and utility-based. Furthermore, a minimax approximation and a Nikaido-Isado function-based Nash equilibrium calculation algorithm are proposed in order to calculate the resolved game results. Finally, these two algorithms are applied over a real-life and an arti{fi}cial data set for diﬀerent settings and compared with a linear SVM.},
}