research-article

A comprehensive study on learning-based PE malware family classification methods

Authors:

Keqiu LiAuthors Info & Claims

ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 1314 - 1325

https://doi.org/10.1145/3468264.3473925

Published: 18 August 2021 Publication History

Abstract

Driven by the high profit, Portable Executable (PE) malware has been consistently evolving in terms of both volume and sophistication. PE malware family classification has gained great attention and a large number of approaches have been proposed. With the rapid development of machine learning techniques and the exciting results they achieved on various tasks, machine learning algorithms have also gained popularity in the PE malware family classification task. Three mainstream approaches that use learning based algorithms, as categorized by the input format the methods take, are image-based, binary-based and disassembly-based approaches. Although a large number of approaches are published, there is no consistent comparisons on those approaches, especially from the practical industry adoption perspective. Moreover, there is no comparison in the scenario of concept drift, which is a fact for the malware classification task due to the fast evolving nature of malware. In this work, we conduct a thorough empirical study on learning-based PE malware classification approaches on 4 different datasets and consistent experiment settings. Based on the experiment results and an interview with our industry partners, we find that (1) there is no individual class of methods that significantly outperforms the others; (2) All classes of methods show performance degradation on concept drift (by an average F1-score of 32.23%); and (3) the prediction time and high memory consumption hinder existing approaches from being adopted for industry usage.

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Index Terms

A comprehensive study on learning-based PE malware family classification methods
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Malware and its mitigation

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cover image ACM Conferences

ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 2021

1690 pages

ISBN:9781450385626

DOI:10.1145/3468264

General Chairs:
Diomidis Spinellis
Athens University of Economics and Business, Greece
,
Georgios Gousios
Facebook, Netherlands / Delft University of Technology, Netherlands
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Massimiliano Di Penta
University of Sannio, Italy

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Published: 18 August 2021

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the NSFC-General Technology Basic Research Joint Funds under Grant
The NSFC Youth Funds under Grant
State Key Laboratory of Communication Content Cognition fund
the National Key Research and Development Program of China

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ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 23 - 28, 2021

Athens, Greece

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https://doi.org/10.1016/j.jnca.2024.104050
Zhao HWu YZou DLiu YJin H(2024)MalSensor: Fast and Robust Windows Malware ClassificationACM Transactions on Software Engineering and Methodology10.1145/368883334:1(1-28)Online publication date: 24-Aug-2024
https://dl.acm.org/doi/10.1145/3688833
Su ZXu XHuang ZZhang ZYe YHuang JZhang X(2024)CodeArt: Better Code Models by Attention Regularization When Symbols Are LackingProceedings of the ACM on Software Engineering10.1145/36437521:FSE(562-585)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643752
Jiang JYang JZhang YWang ZYou HChen J(2024)A Post-training Framework for Improving the Performance of Deep Learning Models via Model TransformationACM Transactions on Software Engineering and Methodology10.1145/363001133:3(1-41)Online publication date: 15-Mar-2024
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