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Authors: Miles Q. Li 1 and Benjamin C. M. Fung 2

Affiliations: 1 School of Computer Science, McGill University, Montreal, Canada ; 2 School of Information Studies, McGill University, Montreal, Canada

Keyword(s): Malware Classification, Interpretable Machine Learning, Neural Networks.

Abstract: Malware is the crux of cyber-attacks, especially in the attacks of critical cyber(-physical) infrastructures, such as financial systems, transportation systems, smart grids, etc. Malware classification has caught extensive attention because it can help security personnel to discern the intent and severity of a piece of malware before appropriate actions will be taken to secure a critical cyber infrastructure. Existing machine learning-based malware classification methods have limitations on either their performance or their abilities to interpret the results. In this paper, we propose a novel malware classification model based on functional analysis of malware samples with the interpretability to show the importance of each function to a classification result. Experiment results show that our model outperforms existing state-of-the-art methods in malware family and severity classification and provide meaningful interpretations.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Li, M. and Fung, B. (2022). Interpretable Malware Classification based on Functional Analysis. In Proceedings of the 17th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-588-3; ISSN 2184-2833, SciTePress, pages 500-507. DOI: 10.5220/0011310900003266

@conference{icsoft22,
author={Miles Q. Li. and Benjamin C. M. Fung.},
title={Interpretable Malware Classification based on Functional Analysis},
booktitle={Proceedings of the 17th International Conference on Software Technologies - ICSOFT},
year={2022},
pages={500-507},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011310900003266},
isbn={978-989-758-588-3},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Software Technologies - ICSOFT
TI - Interpretable Malware Classification based on Functional Analysis
SN - 978-989-758-588-3
IS - 2184-2833
AU - Li, M.
AU - Fung, B.
PY - 2022
SP - 500
EP - 507
DO - 10.5220/0011310900003266
PB - SciTePress

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