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Computer Science

Using convolutional neural networks for classification of malware represented as images

Daniel Gibert Llauradó, Carles Mateu, Jordi Planes, R. Vicens

2018.8.27Journal of Computer Virology and Hacking Techniques

DOI: 10.1007/s11416-018-0323-0

tlooto Summary

Motivated by the visual similarity between malware samples of the same family, a file agnostic deep learning approach is proposed to efficiently group malicious software into families based on a set of discriminant patterns extracted from their visualization as images.

Abstract

Abstract is not available.

Citation format

LLAURADÓ, Daniel Gibert, et al. Using convolutional neural networks for classification of malware represented as images. Journal of Computer Virology and Hacking Techniques, 2018, 15: 15–28.