Malware Classification by Ensemble Application of Convolutional and Recurrent Neural Networks

  • Authors

    • Hae Jung Kim
    • . .
    https://doi.org/10.14419/ijet.v7i3.7.19038
  • 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.

     

     

  • References

    1. [1] Chen, M., et al., Big Data Analysis. Big Data. Springer International Publishing, 2014. : p. 51-58.
      http://www.springer.com/in/book/9783319062440

      [2] Luo, X., and Liao, Q. Awareness Education as the key to Ransomware Prevention. Information Systems Security, 2007. 16(4): p. 195-202.

      [3] Sathyanarayan V. S., Kohli P., Bruhadeshwar B., Signature Generation and Detection of Malware Families. Information Security and Privacy. Springer, Berlin, Heidelberg. ACISP 2008. 5107. : p. 336-349 https://doi.org/10.1007/978-3-540-70500-0_25

      [4] Damodaran, A., et al. A comparison of static, dynamic, and hybrid analysis for malware detection. Journal of Computer Virology and Hacking Techniques, 2017. 13(1): p. 1-12.

      [5] Dahl, E., Stokes, J. W., Deng, L., and Yu, D. Large-scale malware classification using random projections and neural networks. Paper presented at the IEEE International Conference on Acoustics, Speech and Signal Processing. 2013. DOI: 10.1109/ICASSP.2013.6638293.

      [6] Kolter, J.Z. and Maloof, M.A., Learning to detect and classify malicious executables in the wild. The Journal of Machine Learning Research, 2006. 7: p. 2721-2744.

      [7] Christodorescum, M., Jha, S., Seshia, S.A., Song, D. and Bryant, R.E. Semantics-Aware Malware Detection. SP '05 Proceedings of the 2005 IEEE Symposium on Security and Privacy, 2005. : p. 32-46.

      [8] Kelly, Nataraj, L., Karthikeyan, S., Jacob, G., and Manjunath, B. S. Malware Images: Visualization and Automatic Classification. Published in Proceedings of the 8th International Symposium on Visualization for Cyber Security, 2011. Article No. 4. DOI:10.1145/2016904.2016908.

      [9] Guyon, I. and Elisseeff, A. An introduction to variable and feature selection. The Journal of Machine Learning Research, 2003. 3: p. 1157-1182.

      [10] Sak, H., Senior, A., and Beaufays, F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Presented at Interspeech. 2014. : p. 338-342.

      [11] Kaggle. Microsoft Microsoft Malware Classiï¬cation Challenge (BIG 2015). Retrieved from https://www.kaggle.com/c/malware-classiï¬cation.

  • Downloads

  • How to Cite

    Jung Kim, H., & ., . (2018). Malware Classification by Ensemble Application of Convolutional and Recurrent Neural Networks. International Journal of Engineering & Technology, 7(3.7), 511-513. https://doi.org/10.14419/ijet.v7i3.7.19038