  The accuracy of deployed malware-detection classifiers degrades over time due
to changes in data distributions and increasing discrepancies between training
and testing data. This phenomenon is known as the concept drift. While the
concept drift can be caused by various reasons in general, new malicious files
are created by malware authors with a clear intention of avoiding detection.
The existence of the intention opens a possibility for predicting such future
samples. Including predicted samples in training data should consequently
increase the accuracy of the classifiers on new testing data.
  We compare two methods for predicting future samples: (1) adversarial
training and (2) generative adversarial networks (GANs). The first method
explicitly seeks for adversarial examples against the classifier that are then
used as a part of training data. Similarly, GANs also generate synthetic
training data. We use GANs to learn changes in data distributions within
different time periods of training data and then apply these changes to
generate samples that could be in testing data. We compare these prediction
methods on two different datasets: (1) Ember public dataset and (2) the
internal dataset of files incoming to Avast. We show that while adversarial
training yields more robust classifiers, this method is not a good predictor of
future malware in general. This is in contrast with previously reported
positive results in different domains (including natural language processing
and spam detection). On the other hand, we show that GANs can be successfully
used as predictors of future malware. We specifically examine malware families
that exhibit significant changes in their data distributions over time and the
experimental results confirm that GAN-based predictions can significantly
improve the accuracy of the classifier on new, previously unseen data.
