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Ensemble Malware Classification Using Neural Networks


[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ S ] student | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Chapter type

chapter in monograph / paper

Publication language


  • Malware detection
  • Microsoft Malware Classification Challenge
  • Malware neural networks

EN This work presents an experimental study of malware classification using the Microsoft Malware Classification Challenge 2015 dataset. We combine the approach of the winning solution to the Microsoft Malware Classification Challenge with the neural network approach. Using a combination of n-grams features for both assembly (asm) and byte code enables us to significantly improve the result. By mixing multiple approaches, we are able to get the best log-loss result of 0.0025, so far. This comes mostly from the classical XGBoost method with n-gram contributions from the binary and assembly code. However, understanding this result is still incomplete. The standard neural network approaches (even with LSTM) alone give poorer results compared to the XGBoost, based on mostly n-gram. It is not clear why adding 6-grams to the binary code analysis does not improve results. There are many more options to be tested in the future, in particular networks.

Pages (from - to)

125 - 138





Multimedia Communications, Services and Security : 10th International Conference, MCSS 2020, Kraków, Poland, October 8-9, 2020, Proceedings

Presented on

10th International Conference on Multimedia Communications, Services and Security, MCSS 2020, 8-9.10.2020, Kraków, Polska

License type


Ministry points / chapter


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