On the Effectiveness of Generic Malware Models

Naman Bagga, Fabio Di Troia, Mark Stamp

Abstract

Malware detection based on machine learning typically involves training and testing models for each malware family under consideration. While such an approach can generally achieve good accuracy, it requires many classification steps, resulting in a slow, inefficient, and potentially impractical process. In contrast, classifying samples as malware or benign based on more generic “families” would be far more efficient. However, extracting common features from extremely general malware families will likely result in a model that is too generic to be useful. In this research, we perform controlled experiments to determine the tradeoff between generality and accuracy—over a variety of machine learning techniques—based on n-gram features.

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Paper Citation


in Harvard Style

Bagga N., Troia F. and Stamp M. (2018). On the Effectiveness of Generic Malware Models.In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2: BASS, ISBN 978-989-758-319-3, pages 442-450. DOI: 10.5220/0006921504420450


in Bibtex Style

@conference{bass18,
author={Naman Bagga and Fabio Di Troia and Mark Stamp},
title={On the Effectiveness of Generic Malware Models},
booktitle={Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2: BASS,},
year={2018},
pages={442-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006921504420450},
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 - On the Effectiveness of Generic Malware Models
SN - 978-989-758-319-3
AU - Bagga N.
AU - Troia F.
AU - Stamp M.
PY - 2018
SP - 442
EP - 450
DO - 10.5220/0006921504420450