  In this paper, we consider malware classification using deep learning
techniques and image-based features. We employ a wide variety of deep learning
techniques, including multilayer perceptrons (MLP), convolutional neural
networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU).
Amongst our CNN experiments, transfer learning plays a prominent role
specifically, we test the VGG-19 and ResNet152 models. As compared to previous
work, the results presented in this paper are based on a larger and more
diverse malware dataset, we consider a wider array of features, and we
experiment with a much greater variety of learning techniques. Consequently,
our results are the most comprehensive and complete that have yet been
published.
