Robust Hashing for Image-based Malware Classification

Wei-Chung Huang, Fabio Di Troia, Mark Stamp

Abstract

In this paper, we compare and contrast support vector machine (SVM) classifiers to robust hashing based strategies for the malware classification problem. For both the SVM and robust hashing approaches, we treat each executable file as a two-dimensional image. We experiment with two image-based robust hashing techniques, one that relies on wavelet analysis, and one that uses distributed coding. For our support vector machine experiments, we consider an image-based feature that deals with horizontal edges. While the SVM performs slightly better, there are some potential advantages to robust hashing for malware detection.

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Paper Citation


in Harvard Style

Huang W., Troia F. and Stamp M. (2018). Robust Hashing for Image-based Malware Classification.In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2: BASS, ISBN 978-989-758-319-3, pages 451-459. DOI: 10.5220/0006942204510459


in Bibtex Style

@conference{bass18,
author={Wei-Chung Huang and Fabio Di Troia and Mark Stamp},
title={Robust Hashing for Image-based Malware Classification},
booktitle={Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2: BASS,},
year={2018},
pages={451-459},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006942204510459},
isbn={978-989-758-319-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2: BASS,
TI - Robust Hashing for Image-based Malware Classification
SN - 978-989-758-319-3
AU - Huang W.
AU - Troia F.
AU - Stamp M.
PY - 2018
SP - 451
EP - 459
DO - 10.5220/0006942204510459