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Authors: Naman Bagga ; Fabio Di Troia and Mark Stamp

Affiliation: Department of Computer Science, San Jose State University, San Jose, California and U.S.A.

ISBN: 978-989-758-319-3

Keyword(s): Malware, Support Vector Machine, k-nearest Neighbor, Chi-squared Test, Random Forest.

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 several formats:
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

@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},
}

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

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