  Deep learning has been used in the research of malware analysis. Most
classification methods use either static analysis features or dynamic analysis
features for malware family classification, and rarely combine them as
classification features and also no extra effort is spent integrating the two
types of features. In this paper, we combine static and dynamic analysis
features with deep neural networks for Windows malware classification. We
develop several methods to generate static and dynamic analysis features to
classify malware in different ways. Given these features, we conduct
experiments with composite neural network, showing that the proposed approach
performs best with an accuracy of 83.17% on a total of 80 malware families with
4519 malware samples. Additionally, we show that using integrated features for
malware family classification outperforms using static features or dynamic
features alone. We show how static and dynamic features complement each other
for malware classification.
