Malware Classification by Ensemble Application of Convolutional and Recurrent Neural Networks


  • Hae Jung Kim
  • . .



Malware classification, convolutional neural network (CNN), recurrent neural network (RNN)


Malicious software written for malevolent purposes poses a serious threat to information security. With respect to information security for malware treatment, malicious codes must be correctly classified. In this paper, we propose an ensemble classification scheme for the convolutional neural network and recurrent neural network models. We then analyze the classification results of malicious software. These results are presented as a confusion matrix and cosine similarity. The performances of the classifiers are compared and visualized by using graphical representations. The performance of the proposed ensemble model was the highest at 96.50%, indicating its viability as an accurate classification model.




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