  In this paper, we present a novel method of differentiating known from
previously unseen malware families. We utilize transfer learning by learning
compact file representations that are used for a new classification task
between previously seen malware families and novel ones. The learned file
representations are composed of static and dynamic features of malware and are
invariant to small modifications that do not change their malicious
functionality. Using an extensive dataset that consists of thousands of
variants of malicious files, we were able to achieve 97.7% accuracy when
classifying between seen and unseen malware families. Our method provides an
important focalizing tool for cybersecurity researchers and greatly improves
the overall ability to adapt to the fast-moving pace of the current threat
landscape.
