  With the rapid growth of the number of devices on the Internet, malware poses
a threat not only to the affected devices but also their ability to use said
devices to launch attacks on the Internet ecosystem. Rapid malware
classification is an important tools to combat that threat. One of the
successful approaches to classification is based on malware images and deep
learning. While many deep learning architectures are very accurate they usually
take a long time to train. In this work we perform experiments on multiple well
known, pre-trained, deep network architectures in the context of transfer
learning. We show that almost all them classify malware accurately with a very
short training period.
