  We propose to apply deep transfer learning from computer vision to static
malware classification. In the transfer learning scheme, we borrow knowledge
from natural images or objects and apply to the target domain of static malware
detection. As a result, training time of deep neural networks is accelerated
while high classification performance is still maintained. We demonstrate the
effectiveness of our approach on three experiments and show that our proposed
method outperforms other classical machine learning methods measured in
accuracy, false positive rate, true positive rate and $F_1$ score (in binary
classification). We instrument an interpretation component to the algorithm and
provide interpretable explanations to enhance security practitioners' trust to
the model. We further discuss a convex combination scheme of transfer learning
and training from scratch for enhanced malware detection, and provide insights
of the algorithmic interpretation of vision-based malware classification
techniques.
