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Deep learning has been used in the research of malware analysis. Mostclassification methods use either static analysis features or dynamic analysisfeatures for malware family classification, and rarely combine them asclassification features and also no extra effort is spent integrating the twotypes of features. In this paper, we combine static and dynamic analysisfeatures with deep neural networks for Windows malware classification. Wedevelop several methods to generate static and dynamic analysis features toclassify malware in different ways. Given these features, we conductexperiments with composite neural network, showing that the proposed approachperforms best with an accuracy of 83.17% on a total of 80 malware families with4519 malware samples. Additionally, we show that using integrated features formalware family classification outperforms using static features or dynamicfeatures alone. We show how static and dynamic features complement each otherfor malware classification.

Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural Network