research-article

On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection

Authors:

Tegawendé F. Bissyandé,

John GrundyAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 30, Issue 3

Article No.: 40, Pages 1 - 38

https://doi.org/10.1145/3446905

Published: 08 May 2021 Publication History

Abstract

Malware detection at scale in the Android realm is often carried out using machine learning techniques. State-of-the-art approaches such as DREBIN and MaMaDroid are reported to yield high detection rates when assessed against well-known datasets. Unfortunately, such datasets may include a large portion of duplicated samples, which may bias recorded experimental results and insights. In this article, we perform extensive experiments to measure the performance gap that occurs when datasets are de-duplicated. Our experimental results reveal that duplication in published datasets has a limited impact on supervised malware classification models. This observation contrasts with the finding of Allamanis on the general case of machine learning bias for big code. Our experiments, however, show that sample duplication more substantially affects unsupervised learning models (e.g., malware family clustering). Nevertheless, we argue that our fellow researchers and practitioners should always take sample duplication into consideration when performing machine-learning-based (via either supervised or unsupervised learning) Android malware detections, no matter how significant the impact might be.

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Index Terms

On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection
1. Computing methodologies
  1. Machine learning
2. Software and its engineering
  1. Software notations and tools

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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 30, Issue 3

Continuous Special Section: AI and SE

July 2021

600 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3450566

Editor:
Mauro Pezzè
Università della Svizzera italiana and Università di Milano-Bicocca, Switzerland

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Copyright © 2021 ACM.

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Publication History

Published: 08 May 2021

Accepted: 01 January 2021

Revised: 01 December 2020

Received: 01 March 2020

Published in TOSEM Volume 30, Issue 3

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Discovery Early Career Researcher Award (DECRA)
European Union's Horizon 2020 research and innovation program
National Natural Science Foundation of China
Discovery project
Fonds National de la Recherche (FNR), Luxembourg, under project CHARACTERIZE
SPARTA project
Australian Research Council (ARC) under a Laureate Fellowship

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https://dl.acm.org/doi/10.61822/amcs-2024-0021
Huckvale EMoseley H(2024)A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvementPLOS ONE10.1371/journal.pone.029958319:5(e0299583)Online publication date: 2-May-2024
https://doi.org/10.1371/journal.pone.0299583
Gao CYang WYe JXue YSun J(2024)sGuard+: Machine Learning Guided Rule-Based Automated Vulnerability Repair on Smart ContractsACM Transactions on Software Engineering and Methodology10.1145/364184633:5(1-55)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3641846
Maray MMaashi MAlshahrani HAljameel SAbdelbagi SSalama A(2024)Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware DetectionIEEE Access10.1109/ACCESS.2024.335794412(24516-24524)Online publication date: 2024
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Jeremiah SChen HGritzalis SPark J(2024)Leveraging application permissions and network traffic attributes for Android ransomware detectionJournal of Network and Computer Applications10.1016/j.jnca.2024.103950230(103950)Online publication date: Oct-2024
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