  Analyzing a huge amount of malware is a major burden for security analysts.
Since emerging malware is often a variant of existing malware, automatically
classifying malware into known families greatly reduces a part of their burden.
Image-based malware classification with deep learning is an attractive approach
for its simplicity, versatility, and affinity with the latest technologies.
However, the impact of differences in deep learning models and the degree of
transfer learning on the classification accuracy of malware variants has not
been fully studied. In this paper, we conducted an exhaustive survey of deep
learning models using 24 ImageNet pre-trained models and five fine-tuning
parameters, totaling 120 combinations, on two platforms. As a result, we found
that the highest classification accuracy was obtained by fine-tuning one of the
latest deep learning models with a relatively low degree of transfer learning,
and we achieved the highest classification accuracy ever in cross-validation on
the Malimg and Drebin datasets. We also confirmed that this trend holds true
for the recent malware variants using the VirusTotal 2020 Windows and Android
datasets. The experimental results suggest that it is effective to periodically
explore optimal deep learning models with the latest models and malware
datasets by gradually reducing the degree of transfer learning from half.
