Abstract
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.
Supported by PUT statutory funds. One of the authors (CJ) acknowledges the NVIDIA GPU Grant of Quadro P6000 card.
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Wyrwinski, P., Dutkiewicz, J., Jedrzejek, C. (2020). Ensemble Malware Classification Using Neural Networks. In: Dziech, A., Mees, W., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2020. Communications in Computer and Information Science, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-59000-0_10
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