  I describe an optimal control view of adversarial machine learning, where the
dynamical system is the machine learner, the input are adversarial actions, and
the control costs are defined by the adversary's goals to do harm and be hard
to detect. This view encompasses many types of adversarial machine learning,
including test-item attacks, training-data poisoning, and adversarial reward
shaping. The view encourages adversarial machine learning researcher to utilize
advances in control theory and reinforcement learning.
