Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
  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.
