Title: Adversarial Deep Learning for Robust Detection of Binary Encoded Malware,
Abstract: Malware is constantly adapting in order to avoid detection. Model based
malware detectors, such as SVM and neural networks, are vulnerable to so-called
adversarial examples which are modest changes to detectable malware that allows
the resulting malware to evade detection. Continuous-valued methods that are
robust to adversarial examples of images have been developed using saddle-point
optimization formulations. We are inspired by them to develop similar methods
for the discrete, e.g. binary, domain which characterizes the features of
malware. A specific extra challenge of malware is that the adversarial examples
must be generated in a way that preserves their malicious functionality. We
introduce methods capable of generating functionally preserved adversarial
malware examples in the binary domain. Using the saddle-point formulation, we
incorporate the adversarial examples into the training of models that are
robust to them. We evaluate the effectiveness of the methods and others in the
literature on a set of Portable Execution~(PE) files. Comparison prompts our
introduction of an online measure computed during training to assess general
expectation of robustness.