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Learning Convolutional Malware Classifiers On Raw Executables - Marek Krčál

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Learning Convolutional Malware Classifiers On Raw Executables - Marek Krčál

We propose and evaluate a simple convolutional deep neural network architecture detecting malicious Portable Executables (Windows executable files) by learning from their raw sequences of bytes and labels only, that is, without any domain-specific feature extraction nor preprocessing. On a dataset of 20 million unpacked half megabyte Portable Executables, such end-to-end approach achieves performance almost on par with the traditional machine learning pipeline based on handcrafted features of Avast.

Speaker: Marek Krčál
Studied Mathematics at Charles University and VU Amsterdam. During his PhD (Charles University) and postdoc (IST Austria) he worked on the boundary of theoretical computer science and algebraic topology. Then he switched to deep learning research as Avast Fellow at Academy of Sciences.

Program:
- 18:00 The talk
- 20:00 Networking in Bitcoin Café

Language: English

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