Interpretable Malware Classification based on Functional Analysis

Miles Q. Li, Benjamin C. M. Fung

2022

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.

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Paper Citation


in Harvard Style

Li M. and Fung B. (2022). Interpretable Malware Classification based on Functional Analysis. In Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-588-3, pages 500-507. DOI: 10.5220/0011310900003266


in Bibtex Style

@conference{icsoft22,
author={Miles Li and Benjamin Fung},
title={Interpretable Malware Classification based on Functional Analysis},
booktitle={Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2022},
pages={500-507},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011310900003266},
isbn={978-989-758-588-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Interpretable Malware Classification based on Functional Analysis
SN - 978-989-758-588-3
AU - Li M.
AU - Fung B.
PY - 2022
SP - 500
EP - 507
DO - 10.5220/0011310900003266