  There is a growing body of literature showing that deep neural networks are
vulnerable to adversarial input modification. Recently this work has been
extended from image classification to malware classification over boolean
features. In this paper we present several new methods for training restricted
networks in this specific domain that are highly effective at preventing
adversarial perturbations. We start with a fully adversarially resistant neural
network that has hard non-negative weight restrictions and is equivalent to
learning a monotonic boolean function and then attempt to relax the constraints
to improve classifier accuracy.
