Using Machine Learning for Operational Decisions in Adversarial Environments.

Classical supervised learning assumes that training data is representative of the data expected to be observed in the future. This assumption is clearly violated when an intelligent adversary actively tries to deceive the learner by generating instances very different from those previously seen. The literature on adversarial machine learning aims to address this problem, but often assumes constraints that sophisticated and determined adversaries need not abide by. We model the adversarial machine learning problem by considering an unconstrained, but utilitymaximizing, adversary. In addition, rather than modifying the learning algorithm to increase its robustness to adversarial manipulation, we use an output of an arbitrary probabilistic classifier (such as Naive Bayes) in a linear optimization program that computes optimal randomized operational decisions based on machine learning predictions, operational constraints, and our adversarial model. Our approach is simpler than its predecessors, highly scalable, and we experimentally demonstrate that it outperforms the state of the art on several metrics.